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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. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Goes beyond annotations by explaining the internal mechanism: 'BGE-base-en embeddings + cosine over 500-char overlapping windows', and limitations: 'cap is 200K chars (longer inputs are truncated and flagged)'. No contradiction with readOnlyHint, idempotentHint, or destructiveHint.

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?

Four sentences, tightly structured. First sentence declares action and scope. Second gives usage guidance. Third explains output and pairing. Fourth reveals technical details and limits. Every sentence adds value without redundancy.

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 three parameters and no output schema, the description adequately covers: what it does, how it works (embeddings, windowing), usage context, output format (passages with offsets and scores), and limitations (200K cap, truncation). No gaps for effective agent invocation.

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% with descriptions, but the description enriches beyond: explains that 'text' is already fetched, gives query examples, and specifies 'limit' range (1-20) and default (5). This adds meaningful usage context not in the schema.

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 title 'Search Within a Source' and opening sentence 'Semantic search INSIDE a fetched record' immediately convey the specific action. Examples like SEC 10-K body and article make the resource type concrete. Distinguishes from siblings such as ask_pipeworx_grounded by emphasizing extraction vs. grounding.

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?

Explicitly states when to use: 'when the record is too big to cram into the prompt'. Provides alternative: 'Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document.' This gives clear context and exclusion guidance.

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

A3.8/5.0
Disambiguation2/5

Several tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded; multiple Polymarket tools) and many serve unrelated domains, making it hard for an agent to distinguish which tool to use for a given task, especially given the server's holiday theme.

Naming Consistency3/5

Most tool names follow a lowercase_with_underscores pattern, but the prefixes vary (ask_pipeworx, pipeworx_*, polymarket_*, etc.) and some names are less descriptive (e.g., process, run, execute-like vague verbs are absent, but still the naming lacks a unified convention across the broad set.

Tool Count2/5

35 tools is excessive for a server named 'Openholidays'. The vast majority of tools (e.g., SEC filings, Polymarket, npm scanning) are unrelated to holidays, making the tool count feel bloated and unfocused.

Completeness3/5

For the holiday domain, the server includes necessary tools (list_countries, list_subdivisions, public_holidays, school_holidays) and is complete. However, the server's actual scope is far broader, and many unrelated tools are present, which dilutes the completeness for its stated purpose.