Skip to main content
Glama

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".

TDQS

A4.3/5.0
Behavior5/5

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

Adds specific technical details beyond annotations: uses BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flag. Annotations already indicate idempotent and non-destructive, and description aligns 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.

Conciseness4/5

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

Description is focused and front-loaded with core functionality. Every sentence adds information, though it could be slightly more terse without losing clarity.

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?

Despite no output schema, description fully explains return values (passages with offsets and scores). Includes pairing advice with ask_pipeworx_grounded and truncation behavior, making it self-sufficient for an agent.

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 covers all 3 parameters (100% coverage). Description adds value by explaining the nature of 'query' with natural-language examples and indicating 'limit' controls top-N passages, plus the result format (offsets, scores). Slightly above baseline due to useful examples.

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

Purpose4/5

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 examples (SEC 10-K, article). Distinguishes from siblings by specifying it operates on provided text, but does not explicitly contrast with similar tools like search_geonames.

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

Usage Guidelines4/5

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

Explicitly advises using when the record is too large to include directly, saving context. Mentions pairing with ask_pipeworx_grounded for grounding over passages, but does not explicitly list conditions when not to use.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded; multiple polymarket_* scanners), and the mix of unrelated domains makes it hard to tell which tool is canonical for a task. The few geographic tools are distinct, but they are buried among dozens of non-geographic tools.

Naming Consistency2/5

Naming is a mix of verb_noun (search_geonames, resolve_entity), proper-noun prefixes (pipeworx_*, polymarket_*), and descriptive phrases (ask_pipeworx, bet_research, scan_competitor_ai_presence). No consistent pattern or verb style across the set.

Tool Count2/5

35 tools is far more than a Geonames-focused server needs, and most are unrelated to geospatial data. The count would be borderline for a general data platform, but it is excessive and unfocused for the stated server name.

Completeness1/5

The geographic surface is severely incomplete: only four tools (search_geonames, get_nearby, find_postal_codes, get_timezone) cover a tiny sliver of typical Geonames functionality like reverse geocoding, elevation, or distance calculations. The many non-geographic tools do not compensate for the missing core domain coverage.