// MCP TOKEN COST

The IA-QA — 130+ QA & Dev Tools for AI Agents MCP server costs 24,084 tokens before the first call.

Connect IA-QA — 130+ QA & Dev Tools for AI Agents and its 146 tool definitions are loaded into the model's context on every request — 12% of a 200k window spent before your agent does anything.

QUICK ANSWER The IA-QA — 130+ QA & Dev Tools for AI Agents MCP server's tool definitions consume 24,084 tokens — 13× the median MCP server (1,905 tokens). A scoped grant exposing only the tools you use cuts that roughly in proportion.

MEASURED FROM SCHEMAS 146 tools · 24,084 tokens · 12% of 200k · 2.4% of 1M Method →

// CONTEXT WINDOW SHARE

What that buys before your agent starts working.

Tool definitions are overhead: they occupy context on every request and compete with your code, documents and conversation history for the same window.

200K WINDOW 12%

1M WINDOW 2.4%

Corpus context: IA-QA — 130+ QA & Dev Tools for AI Agents ranks #40 of 3,213 measured MCP servers by definition cost. The median is 1,905 tokens, p90 is 7,952, and the heaviest (Fusionauth) is 183,337 — 92% of a 200k window on its own.

// PER-TOOL BREAKDOWN

Where the 24,084 tokens go.

Each row is one tool definition as a tools/list entry — name, description and input schema — counted with o200k_base. Average: 165 tokens per tool.

Tool	Category	Tokens	% of server
run_vlm_test_suite	Execute	546	2.3%
run_vlm_test_suite_batch	Execute	542	2.3%
multimodal_eval_guide	Write	516	2.1%
jira_to_test_suite	Read	446	1.9%
run_semantic_tests	Execute	399	1.7%
validate_agent_trajectory	Read	395	1.6%
transform_json_array	Execute	352	1.5%
shield_analyze	Read	309	1.3%
create_confluence_page	Write	299	1.2%
llm_fit_finder	Read	297	1.2%
llm_output_validator	Read	296	1.2%
test_skill	Read	294	1.2%
llm_generate	Write	283	1.2%
validate_mcp_response	Read	278	1.2%
similarity_score	Read	273	1.1%
prompt_test_suite	Read	268	1.1%
embedding_similarity	Read	261	1.1%
fetch_jira_issue	Read	260	1.1%
search_jira_issues	Read	253	1.1%
fetch_confluence_page	Read	248	1.0%
generate_eval_yaml	Write	246	1.0%
rerank_evaluate	Read	245	1.0%
run_eval_contract	Execute	243	1.0%
build_rag_prompt	Execute	232	1.0%
fix_gherkin	Read	226	0.9%
token_budget_calculator	Read	225	0.9%
needle_haystack_generate	Write	224	0.9%
secret_scan	Read	219	0.9%
get_testing_guidelines	Read	213	0.9%
web_security_audit	Execute	201	0.8%
guardrail_test	Read	197	0.8%
system_prompt_builder	Execute	195	0.8%
generate_curl	Write	194	0.8%
normalize_whitespace	Read	193	0.8%
pr_gatekeeper	Read	192	0.8%
compare_responses	Read	191	0.8%
estimate_llm_cost	Read	190	0.8%
latency_benchmark	Read	187	0.8%
post_jira_comment	Write	186	0.8%
find_tool	Read	180	0.7%
few_shot_formatter	Read	176	0.7%
bias_detect	Read	175	0.7%
consistency_check	Read	173	0.7%
security_headers_check	Read	171	0.7%
mcp_server_evaluate	Execute	170	0.7%
function_call_validate	Read	170	0.7%
bm25_score	Read	168	0.7%
ab_test_report	Read	164	0.7%
redact_pii	Read	164	0.7%
generate_json_ld	Write	163	0.7%
sort_lines	Read	162	0.7%
hallucination_check	Read	158	0.7%
generate_hmac	Write	158	0.7%
levenshtein_distance	Write	157	0.7%
context_window_check	Read	156	0.6%
regex_test	Read	155	0.6%
json_schema_generate	Write	155	0.6%
cors_test	Read	153	0.6%
format_bytes	Read	153	0.6%
number_base_convert	Write	152	0.6%
list_llm_models	Read	151	0.6%
lorem_ipsum	Read	151	0.6%
run_pr_gate_pipeline	Execute	149	0.6%
compare_models	Read	148	0.6%
mock_from_schema	Read	147	0.6%
llm_format_check	Read	145	0.6%
vector_similarity	Read	145	0.6%
cookie_security_audit	Write	145	0.6%
diff_text	Read	144	0.6%
vector_stats	Read	144	0.6%
extract_todos	Read	143	0.6%
truncate_to_tokens	Destructive	142	0.6%
ssl_certificate_check	Read	142	0.6%
openapi_validate	Read	141	0.6%
optimize_prompt_tokens	Read	141	0.6%
generate_password	Write	141	0.6%
word_frequency	Read	140	0.6%
cors_checker	Read	139	0.6%
response_quality_score	Read	138	0.6%
xml_to_json	Read	138	0.6%
fetch_veille_feed	Read	136	0.6%
parse_csv	Execute	133	0.6%
vector_quantize	Execute	133	0.6%
count_code_lines	Read	130	0.5%
json_schema_validate	Read	130	0.5%
case_convert	Write	130	0.5%
prompt_template_fill	Write	130	0.5%
flatten_json	Read	129	0.5%
color_convert	Write	129	0.5%
merge_json	Write	129	0.5%
cot_analyzer	Read	128	0.5%
cron_validator	Read	128	0.5%
format_table	Read	127	0.5%
model_info	Read	127	0.5%
yaml_to_json	Read	124	0.5%
hash_text	Read	122	0.5%
detect_secrets	Read	120	0.5%
mcp_server_health_check	Read	120	0.5%
json_diff	Read	118	0.5%
analyze_diff_bugs	Read	117	0.5%
normalize_vector	Read	115	0.5%
split_chunks	Read	115	0.5%
rag_relevance_rank	Read	114	0.5%
toxicity_scan	Read	114	0.5%
env_parse	Execute	113	0.5%
generate_test_cases	Write	113	0.5%
parse_http_headers	Execute	112	0.5%
extract_json_path	Read	112	0.5%
lint_commit_message	Write	112	0.5%
generate_slug	Write	109	0.5%
llm_json_schema_check	Read	108	0.4%
score_geo_signals	Read	108	0.4%
identify_caller	Read	107	0.4%
extract_links	Read	106	0.4%
extract_json_from_text	Read	105	0.4%
json_to_csv	Read	104	0.4%
html_to_markdown	Read	102	0.4%
conversation_analyze	Read	101	0.4%
prompt_injection_scan	Read	101	0.4%
timestamp_convert	Write	101	0.4%
webhook_endpoint_requests	Read	99	0.4%
decode_jwt	Read	98	0.4%
detect_language	Read	98	0.4%
unescape_html	Read	98	0.4%
check_contrast_ratio	Read	97	0.4%
generate_html_report	Write	97	0.4%
url_encode	Read	96	0.4%
minify_js	Read	95	0.4%
count_tokens	Read	94	0.4%
generate_uuid	Write	94	0.4%
strip_markdown	Execute	93	0.4%
http_status_lookup	Read	90	0.4%
webhook_endpoint_create	Write	89	0.4%
json_to_yaml	Read	88	0.4%
text_stats	Read	87	0.4%
escape_html	Read	85	0.4%
list_local_tests	Read	83	0.3%
cron_parse	Execute	82	0.3%
validate_email	Read	81	0.3%
mcp_schema_lint	Read	78	0.3%
calculate_readability	Read	76	0.3%
format_json	Read	76	0.3%
base64_encode	Read	75	0.3%
base64_decode	Read	72	0.3%
url_decode	Read	66	0.3%
validate_url	Read	64	0.3%

// SCOPED GRANT

Most agents use a handful of these tools. They pay for all 146.

A PolicyLayer grant exposes only the tools you allow — ungranted definitions are filtered out of the tool list, so they never enter the context window. Estimates below assume typical-weight tools (165 tokens each).

Grant scope	Definition cost	Reduction
All 146 tools (no gateway)	24,084 tokens	—
3 granted tools	~495 tokens	−98%
5 granted tools	~825 tokens	−97%
10 granted tools	~1,650 tokens	−93%

Model your own stack in the token-cost calculator, or see the IA-QA — 130+ QA & Dev Tools for AI Agents policy for what a sensible grant looks like.

//FAQ

IA-QA — 130+ QA & Dev Tools for AI Agents token-cost questions.

How many tokens does the IA-QA — 130+ QA & Dev Tools for AI Agents MCP server use?+

Its 146 tool definitions total 24,084 tokens — 12% of a 200k context window — measured with tiktoken o200k_base over the serialised tools/list payload. Exact counts vary slightly by client and model.

Why does IA-QA — 130+ QA & Dev Tools for AI Agents consume tokens before I send a message?+

MCP clients load every connected server's tool definitions — name, description, and input schema — into the model's context so it knows what it can call. That payload is charged against your context window on every request, whether or not a tool is used.

How do I reduce IA-QA — 130+ QA & Dev Tools for AI Agents's token usage?+

Expose fewer tools. A PolicyLayer grant scopes IA-QA — 130+ QA & Dev Tools for AI Agents to only the tools you allow — ungranted definitions are filtered out of the tool list, so they never enter the context window. A grant of 3 typical tools costs roughly 495 tokens, a 98% reduction.

Does deferred tool loading fix this?+

Partially, in some clients. Claude Code defers MCP tool schemas behind a tool-search step by default, and VS Code has experimental grouping — but you still pay tokens per search and reload, and Cursor, Windsurf and Gemini CLI load definitions upfront. Reducing the exposed tool set cuts the cost in every client.

// METHOD & REVIEW

How these numbers were measured.

01

Serialisation

Each tool is serialised as a tools/list entry — name, description, input schema — from the schemas in the PolicyLayer scan database. Clients differ slightly in framing, so treat counts as close estimates.

02

Tokeniser

tiktoken o200k_base (GPT-4o/o-series). Anthropic's current tokeniser isn't published, so Claude's exact counts will differ; for English text and JSON schemas the totals are close enough to treat these as estimates.

03

Deferred loading

Some clients now defer schema loading (Claude Code's tool search; VS Code experimental grouping). You still pay per search and reload — and Cursor, Windsurf and Gemini CLI load everything upfront.

Computed 07-06-2026 from the PolicyLayer scan database over all 146 catalogued IA-QA — 130+ QA & Dev Tools for AI Agents tools. Counts refresh with every site build.