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Glama

catfacts

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 behavioral details beyond annotations: it returns top-N passages with character offsets and similarity scores, specifies underlying embeddings (BGE-base-en) and chunking (500-char windows), and states the 200K char limit with truncation behavior. This enriches the safe-read profile defined by annotations.

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 dense but every sentence serves a purpose: main action, use case, pairing info, technical details, and limits. It is front-loaded with the core purpose and structured logically, with 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 no output schema, the description compensates by describing return format (passages with offsets and scores), technical behavior (embedding model, window size), and edge cases (truncation). Combined with 100% schema coverage and strong annotations, the description fully covers what an agent needs.

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 contextual meaning by giving examples of 'text' (SEC 10-K, article) and query examples, plus the practical motivation for passing text directly. This adds value beyond the schema's basic parameter descriptions.

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, specifying the resource (record text) and the action (search). It distinguishes itself from siblings by positioning it as a context-saving alternative to ask_pipeworx_grounded and by emphasizing character offsets for verification.

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?

Explicit usage guidance is provided: use when the record is too large for the prompt, pair with ask_pipeworx_grounded, and leverage offsets to verify quotes. This gives clear when-to-use and alternative tool connections.

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

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TDQS

A3.6/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical in routing, differing only in response mode; bet_research and polymarket_edges both surface betting opportunities. Even with detailed descriptions, an agent could easily select the wrong one for a given task.

Naming Consistency2/5

Tool names are all snake_case, but the pattern is inconsistent: some are verb_noun (get_fact, list_breeds, validate_claim), some are noun/adjective compounds (entity_profile, deep_research, bet_research), and several use a pipeworx_ prefix. There is no consistent verb style or object-first convention.

Tool Count2/5

34 tools is over the 25-tool threshold for a server whose name suggests a narrow cat-facts focus. Only 3 tools relate to cat facts; the rest form a sprawling data platform, creating a severe scope mismatch that makes the count feel excessive and unfocused.

Completeness3/5

For the cat-facts domain, the set covers the essentials: single fact, multiple facts, and breed listing. However, the overall tool surface is a mix of unrelated capabilities (data lookups, memory, subscriptions, prediction markets) that don't form a coherent domain, leaving the cat-facts portion sparse and the broader set without clear lifecycle coverage.

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