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Postscript

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

A4.4/5.0
Behavior5/5

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

Annotations already show readOnlyHint=true, idempotentHint=true, etc. Description adds significant detail: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flagging, and output includes offsets for 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.

Conciseness4/5

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

Front-loaded with the core purpose, then explains usage context, technical details, and pairing. Every sentence adds value, though the paragraph could be broken into two for easier scanning. Efficient but not perfectly 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?

No output schema, but description fully explains return values (top-N passages with offsets and scores), embedding model, windowing strategy, character cap and truncation behavior. Also explains integration with ask_pipeworx_grounded. Covers all needed context for a semantic search tool.

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 description coverage is 100%, so baseline is 3. The description reinforces the text max length and provides query examples, but adds little beyond the schema descriptions. It meets the baseline but doesn't significantly exceed it.

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 it performs 'semantic search INSIDE a fetched record' with specific examples like SEC 10-K. It uses a strong verb-resource combination and distinguishes itself from sibling tools like ask_pipeworx_grounded by focusing on internal search within already-fetched text.

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 says 'Use when the record is too big to cram into the prompt' and 'saves context, returns only the passages that matter'. Mentions pairing with ask_pipeworx_grounded, though it doesn't explicitly state when not to use or list alternatives beyond that.

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
Disambiguation4/5

Most tools have clear, distinct purposes with detailed descriptions that differentiate them. However, there is some overlap among data query tools (e.g., ask_pipeworx, deep_research, entity_profile) and among Polymarket analysis tools, which could cause confusion for an agent.

Naming Consistency2/5

Tool names lack a consistent pattern, mixing snake_case (ai_visibility_check, bet_research) with descriptive phrases (ask_pipeworx, generate_llms_txt) and some with verbs (list_subscriptions, remember). This inconsistency makes it harder to predict tool names.

Tool Count3/5

With 34 tools, the server covers a broad scope including data querying, Polymarket analysis, SMS management, and utilities. While many tools are justified, the number feels slightly high and some tools (e.g., multiple Polymarket tools) might be consolidated.

Completeness4/5

The server provides comprehensive coverage for data analysis, entity resolution, fact-checking, and monitoring. However, SMS management lacks create/update operations for keywords and subscribers, and there is no tool for sending SMS messages, indicating minor gaps.

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