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Glama

Fbi Artcrimes

by io.github.pipeworx-io

Search Within a Source

search_within
Read-onlyIdempotent

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe document text to search inside (max ~200K chars).
limitNoMax passages to return (1-20, default 5).
queryYesNatural-language query — what passages do you want? E.g. "supply-chain risk", "fiscal year 2024 revenue", "drug interactions with warfarin".

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses that it uses BGE-base-en embeddings with cosine similarity over 500-char overlapping windows, a 200K char cap with truncation flagging. Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and nondestructive nature, so the description adds valuable implementation details without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (three sentences) with front-loaded purpose. Every sentence adds value: purpose, usage scenario, technical details. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the absence of an output schema, the description adequately explains return values (passages with offsets and scores). It also covers input constraints (200K char cap) and pairing with sibling tools, making it complete for an AI agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds context by providing example queries and stating the text parameter has a ~200K char limit. It does not explicitly mention the limit parameter, but implies it with 'top-N'. Slightly above baseline due to practical examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs semantic search inside a fetched record, using specific verbs and resources. It also distinguishes from the sibling tool ask_pipeworx_grounded by explicitly explaining the pairing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool: when the record is too large for the prompt. It also provides context on how it saves context and returns passage offsets for verification, and mentions pairing with ask_pipeworx_grounded.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Fbi Artcrimes toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical routers (beta is currently exactly the same as stable), and the Polymarket tools (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, bet_research) all scan or price prediction-market opportunities with fuzzy boundaries. Other clusters like memory and subscriptions are clear, but enough overlap remains that an agent can easily misroute a query.

Naming Consistency3/5

Most names are readable snake_case and many follow a verb_noun shape (list_art_crimes, resolve_entity, validate_claim), but the set also contains noun-phrase names (entity_profile, recent_alerts, pipeworx_feedback, deep_research) and brand-prefixed composites (polymarket_kalshi_spread, ask_pipeworx_beta). This is mixed but still scannable; there is no outright convention chaos.

Tool Count2/5

33 tools exceeds the 25+ threshold and is bloated for a server whose name promises FBI art crimes—only two tools relate to that name. Even treated as a Pipeworx platform, the sprawl makes the tool surface harder to navigate than necessary.

Completeness2/5

For the nominal art-crime domain, only list_art_crimes and get_art_crime exist, with no category search, statistics, or art-crime alerting, so an agent expecting art-crime workflows hits dead ends. The unrelated Pipeworx functionality is broadly covered, but that does not make the set complete for its stated server purpose.

Resources