Search Partle's product catalog by name or description. Two distinct modes: - Default (no flags) — fast keyword search. ~100ms. Acts like a normal "dumb" search box: matches the literal words you typed against product names and descriptions, with stemming. Good for queries where the user knows th...
Risk signalsAccepts freeform code/query input (query) · High parameter count (11 properties)
Part of the Partle server.
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AI agents call search_products to retrieve information from Partle 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 search_products 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": {
"search_products": {}
}
}See the full Partle policy for all 21 tools.
These attack patterns abuse exactly the kind of access search_products 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.
Search Partle's product catalog by name or description. Two distinct modes: - Default (no flags) — fast keyword search. ~100ms. Acts like a normal "dumb" search box: matches the literal words you typed against product names and descriptions, with stemming. Good for queries where the user knows the product's likely name ("BC547", "Arduino Uno", "Bosch drill"). Returns noisy/wrong results on cross-language or attribute queries ("compost bin" matches Spanish "composta", not real composters). - super_search=True — slow, high-quality. ~1–2s. Run when the user describes what they want rather than naming it: cross-language ("Schraubenzieher Set" → real screwdriver sets even without German catalog entries), attribute-style ("small metal part with a flat head"), or any case where the default returns junk. Embeds the query with voyage-3-large, takes the cosine top-50 over the corpus (with an exact-name precision boost for part numbers), then a cross-encoder reranks them. The two modes are mutually exclusive in practice — pick one based on whether the user knows the product's name or is describing it. Use this when the user asks to find a specific product or browse products matching a query. Prefer over search_stores when the intent is product-led ("find a drill") rather than store-led. Use get_product afterwards if the user wants full details for one specific result. Read-only. No authentication. Rate-limited to 100 requests/hour per IP. Args: query: Free-text search term. In default mode, treated as keywords (each word matched against product text). In super_search=True, treated as a natural-language description. min_price: Lower bound on price in EUR. Omit for no lower bound. Null-priced rows are NOT excluded by this filter — pass has_price=True if you need only priced listings. max_price: Upper bound on price in EUR. Omit for no upper bound. Tip — narrow by budget: min_price=10, max_price=50, sort_by="price_asc", has_price=True. Products without a listed price (a large fraction of the scraped catalog) sort last under either price ordering and are kept in results unless has_price filters them out. tags: Comma-separated tag filter (e.g. "electronics,bluetooth"). Tags are AND-ed together. store_id: Restrict results to a single store. Use the integer id from search_stores results. sort_by: One of price_asc, price_desc, name_asc, newest, oldest. Omit to use the default search-relevance ranking. has_price: When True, exclude products without a listed price (~most of the scraped catalog). Use this for competitive pricing or budget-bounded shopping. When False, return only null-priced listings (rarely useful). Omit to include both. semantic: Legacy flag. Pure vector ordering, ~250ms. Mostly superseded by super_search=True (which uses the same vector retrieval plus a cross-encoder rerank for materially better ordering at the cost of another ~700ms). Keep using it only if you specifically want vector retrieval *without* the rerank. super_search: Enable for natural-language / "describe what I want" queries. ~1–2s. Embeds the query with voyage-3-large, takes the cosine top-50 (with a precision boost for exact-name matches like part numbers / SKUs), then a cross-encoder reranks them. Use whenever the user is describing rather than naming — cross-language ("Schraubenzieher Set"), attribute-style ("small black metal bracket"), or any case where the default keyword path returns junk. Don't combine with cheap browse-style queries where the user typed an exact product name — keyword default is faster there. On relevance_score here: better than the bi-encoder cosine, but still not a "did I find what the user wanted" gauge. Behavior to expect: gibberish or fully-off-topic queries cap around 0.35; loosely-related catalogue clusters can score 0.7+ even when no item truly matches (a "ceramic vase" query in a catalog with no vases but many ceramic flowerpots will still score high). Read the product names before claiming a match. The score is most useful as a relative signal within one result set — a sharp drop between rank N and N+1 marks where the catalog stops being useful for this query. limit: Max results (1–100, default 20). Larger limits are slower and consume rate budget faster. offset: Skip this many results before returning. Use for pagination (offset += limit on each follow-up call). Returns: A list of products. Each includes id, name, price, currency, url, description, store (id/name/address), tags, images, a canonical partle_url, and relevance_score (cosine similarity 0–1 between the query and the product's embedding when a query was provided; None otherwise). Always share partle_url with the user so they can view the listing. Caveat on relevance_score: it is monotonic *within a single search result set* (useful for spotting a big drop-off between rank 3 and rank 4), but its absolute value is not well-calibrated across queries — most results land in 0.55–0.80 regardless of whether the catalog has truly relevant items. Don't infer "this is a great match" from a 0.75 score alone.. It is categorised as a Read tool in the Partle MCP Server, which means it retrieves data without modifying state.
Register the Partle MCP server in PolicyLayer and add a rule for search_products: 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 Partle. Nothing to install.
search_products 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 search_products 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 search_products. 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.
search_products is provided by the Partle MCP server (https://partle.rubenayla.xyz/mcp/). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 21 Partle tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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