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Glama

Data Austin

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. First observed

TDQS

A5/5.0
Behavior5/5

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

Adds rich detail beyond annotations: embedding model, window size, char cap, truncation flag, and offset verification. No contradiction with 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?

Two focused sentences with technical note; every sentence earns its place, no fluff.

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?

Completely describes return values (passages with offsets and scores), constraints, and pairing with sibling tool, despite no output schema.

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

Parameters5/5

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

Adds meaning beyond schema with examples and behavioral constraints (max chars, default limit, natural-language query 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?

Clearly states semantic search inside a fetched record, with specific examples and explicit distinction from siblings like ask_pipeworx_grounded.

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 says when to use (record too big), pairs with alternative tool, and mentions truncation behavior and cap.

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

A4/5.0
Disambiguation3/5

Many tools have overlapping purposes, such as multiple ask_pipeworx variants (beta, grounded) and several prediction market tools (arbitrage, edges, fill risk, spread). While descriptions help differentiate them, the abundance of similar tools makes it easy for an agent to misselect.

Naming Consistency3/5

Tool names are a mix of snake_case with inconsistent prefixes: some use 'ask_', 'polymarket_', 'austin_', while others are isolated verbs (forget, remember) or compound nouns (entity_profile). The pattern is not uniform but still readable.

Tool Count3/5

With 34 tools, the server is heavy. Although each tool seems justified for its niche, the set could be consolidated (e.g., merging ask_pipeworx variants) to reduce clutter. The count feels slightly excessive for the scope.

Completeness4/5

The server covers a broad range of domains: Austin open data, pipeworx data, prediction markets, memory management, and subscriptions. Core workflows are well-supported, with only minor gaps like a missing cross-source search tool.

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