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Data Tours

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

A4.6/5.0
Behavior5/5

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

Discloses embedding model (BGE-base-en), algorithm (cosine similarity, 500-char windows), and limits (200K chars truncation). This adds value beyond readOnlyHint/idempotentHint annotations.

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?

The description is front-loaded with purpose, but includes a somewhat lengthy technical explanation. Every sentence is useful, though it could be slightly more concise.

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 adequately explains return values (passages, offsets, scores) and algorithm. Pairs with sibling tool and covers truncation behavior.

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

Parameters3/5

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

Schema coverage is 100%, and parameter descriptions already cover semantics. The description provides example queries for 'query' but does not add significant new information beyond 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 description clearly states 'Semantic search INSIDE a fetched record' with specific verb (search) and resource (record). It distinguishes itself from sibling tools like ask_pipeworx_grounded by 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?

Explicitly says 'Use when the record is too big to cram into the prompt' and provides direct guidance on when to use and alternative (ask_pipeworx_grounded).

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

A4.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose. Even closely related tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research are well-differentiated by their use cases and safety guarantees. The multiple polymarket tools each focus on a unique aspect (arbitrage, edge scanning, persistence, fill risk, cross-venue spreads), avoiding ambiguity.

Naming Consistency4/5

All tool names use lowercase with underscores, following a mostly verb_noun or domain_prefix_noun pattern (e.g., ask_pipeworx, entity_profile, resolve_entity). A few names like dataset_info and ai_visibility_check deviate slightly from a strict verb_noun structure, but the overall pattern is predictable and readable.

Tool Count4/5

With 33 tools, the server is on the heavier end of the well-scoped range. However, the count is justified by the breadth of functionality: data queries, prediction markets, entity resolution, memory, monitoring, and more. The tools each serve a specific purpose, and none feel redundant.

Completeness4/5

The tool surface covers a wide range of use cases including data retrieval, comparison, research, monitoring, and memory. Minor gaps exist (e.g., no tool for placing prediction market trades or creating/updating Tours Métropole datasets), but these are likely intentional scope choices. Core workflows are well-supported.

Resources