Time Impact Analysis (TIA) — prospective fragnet insertion into a pre-impact baseline schedule. Supports two modes. Single-base mode (legacy): supply baseline_xer_path or baseline_xer_content. All fragnets are inserted into the same shared baseline XER and impact is measured against that shared b...
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AI agents call time_impact_analysis_fragnet to retrieve information from Cpp Cpm Engine without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though time_impact_analysis_fragnet only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
{
"version": "1",
"default": "deny",
"tools": {
"time_impact_analysis_fragnet": {}
}
}See the full Cpp Cpm Engine policy for all 12 tools.
These attack patterns abuse exactly the kind of access time_impact_analysis_fragnet gives an agent. Each links to the full case and the policy that stops it:
Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.
Time Impact Analysis (TIA) — prospective fragnet insertion into a pre-impact baseline schedule. Supports two modes. Single-base mode (legacy): supply baseline_xer_path or baseline_xer_content. All fragnets are inserted into the same shared baseline XER and impact is measured against that shared baseline. The result carries a single_base_disclosure warning explaining this is an AACE 29R-03 §3.7 simplification — acceptable when all events share a single baseline window, but not strict MIP 3.7 Multiple Base. Multi-base mode (AACE 29R-03 MIP 3.7 Multiple Base): supply per_event_bases — a dict keyed by each fragnet's id, with each value a dict containing EITHER xer_path OR xer_content for that event's pre-event contemporaneous baseline. Each fragnet is inserted into its OWN base, impact is measured against THAT base's pre-event finish, and the result carries per_event_methodology, per_event_base_count, and per_event_bases_used (sha256-truncated content hashes for audit reproducibility). The cumulative-impact figure carries cumulative_caveat because the sum of events measured against different bases is NOT a valid joint impact. Exactly ONE of {baseline_xer_path, baseline_xer_content, per_event_bases} must be supplied. Multi-base mode errors out (returning {"error": ...}) if any fragnet id is missing from per_event_bases. Use this tool when modeling delay impact prospectively (e.g. quantifying RFI / change-order delay before settlement). For retrospective windows analysis after the fact, use forensic_windows_analysis (MIP 3.3 windows). Args: baseline_xer_path: server-side pre-impact baseline XER (single-base mode). baseline_xer_content: full text of pre-impact baseline XER (single-base mode, hosted/remote use). per_event_bases: dict {fragnet_id: {"xer_path": "..."} OR {"xer_content": "<full XER text>"}} for AACE MIP 3.7 Multiple Base mode. Example:: { "F1": {"xer_path": "/tmp/bl_pre_F1.xer"}, "F2": {"xer_content": "<XER text>"}, } fragnets: list of fragnet dicts. Each must have: - 'id', 'name', 'liability' (responsible party) - 'activities': list of {code, name, duration_days, calendar_id?} - 'ties': list of {pred, succ, type, lag_days?} Optional: 'description'. output_dir: output dir for TIA_Report.txt + CSV (tempdir if ""). project_name: optional override. Returns: { "report": path to TIA_Report.txt, "impacts_csv": path to TIA_Impact_Details.csv, "baseline": {"project_finish", "critical_count", ...}, "per_fragnet": [{fragnet_id, name, liability, completion_before, completion_after, impact_days, impact_working_days, affected_activities, status, error}, ...], "cumulative_days": int (sum of per-fragnet impacts), "per_event_methodology": str (canonical label), "per_event_base_count": int (count of unique base XERs), "per_event_bases_used": {fragnet_id: sha256_hash8} (multi-base only), "single_base_disclosure": str (single-base only), "cumulative_caveat": str (multi-base only), }. It is categorised as a Read tool in the Cpp Cpm Engine MCP Server, which means it retrieves data without modifying state.
Register the Cpp Cpm Engine MCP server in PolicyLayer and add a rule for time_impact_analysis_fragnet: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Cpp Cpm Engine. Nothing to install.
time_impact_analysis_fragnet is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the time_impact_analysis_fragnet rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for time_impact_analysis_fragnet. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
time_impact_analysis_fragnet is provided by the Cpp Cpm Engine MCP server (https://mcp.criticalpathpartners.ca/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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