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Deepl

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

A4.7/5.0
Behavior5/5

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

Annotations already signal read-only, idempotent, non-destructive behavior, but the description adds concrete behavioral details: character offsets and similarity scores, BGE-base-en embeddings with cosine over 500-char overlapping windows, and the 200K char cap with truncation flag. 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?

Four sentences with front-loaded purpose, then usage, sibling pairing, and technical constraints. Every sentence earns its place; no filler or redundant restating of annotations.

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?

For a tool with no output schema, the description fully explains return contents (passages, offsets, similarity scores), constraints (500-char windows, 200K cap), and its role in the grounded-QA pipeline. Combined with the rich annotations, the context is complete.

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 covers 100% of parameters with descriptions, so the baseline is 3. The description reinforces the semantics ('text you already pulled', 'natural-language query') and provides examples, but does not add new parameter-level meaning beyond what the schema already documents.

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 names a specific verb ('semantic search INSIDE a fetched record'), identifies the resource (the text the user passes in), and distinguishes itself from siblings by contrasting with ask_pipeworx_grounded and focusing on returning passages rather than whole-document Q&A.

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 ('Use when the record is too big to cram into the prompt'), explains the benefit (saves context, returns only passages that matter), and names the alternative workflow (pairs with ask_pipeworx_grounded). This gives clear, actionable usage 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

B3.3/5.0
Disambiguation2/5

Many tools have overlapping functionality, such as ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded, which are essentially the same with minor differences. The polymarket_* family also has five tools with similar names and purposes, making it easy to select the wrong one despite detailed descriptions.

Naming Consistency2/5

Tool names mix verb-first patterns (ask, generate, list, remember) with noun-first patterns (entity_profile, polymarket_arbitrage), and include camelCase like ai_visibility_check. This inconsistent naming style makes the set feel arbitrary and harder to navigate.

Tool Count3/5

With 36 tools, the server exceeds the typical well-scoped range of 3-15. While the multi-purpose nature justifies a larger set, the presence of many near-duplicates (beta/grounded variants, multiple polymarket tools) inflates the count without proportional functional gain.

Completeness4/5

The toolset covers a wide array of domains including translation, entity resolution, financial data, prediction markets, memory, subscriptions, and AI visibility. It appears very comprehensive for its intended multi-purpose server, with no obvious major gaps in core capabilities.

Resources