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Duration

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

A4.9/5.0
Behavior5/5

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

Adds concrete behavioral details beyond annotations: uses BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation. No contradiction with readOnlyHint, openWorldHint etc.

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?

Concise paragraph, front-loaded with purpose, then essential details. No wordiness. Every sentence adds value.

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 explains return value clearly. Covers embedding model, windowing, size limit, and usage context. Complete for a semantic search tool.

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 coverage is 100%, but description adds value by explaining output format (character offsets, similarity scores) and giving example queries. Slight redundancy with schema for text parameter.

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 verb 'search' and resource 'inside a fetched record'. Specifically mentions semantic search and distinguishes from sibling tools by pairing with '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 'Use when the record is too big to cram into the prompt' and explains benefits like saving context and returning only relevant passages. Suggests complementary tool for grounding.

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

A4.1/5.0
Disambiguation3/5

The tool set includes several overlapping tools: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded serve very similar purposes with only subtle differences in behavior (beta/grounded). Additionally, deep_research and validate_claim partially overlap with these. The prediction market tools are numerous but distinct, and memory/duration tools are clear. Overall, an agent would face some confusion when selecting among the ask_pipeworx variants.

Naming Consistency4/5

Most tools use consistent underscore_case with descriptive verb_noun patterns (e.g., list_subscriptions, resolve_entity, validate_claim). A few memory and subscription tools are single-word verbs (forget, recall, remember, subscribe, unsubscribe), which deviates slightly but remains readable. Overall naming is predictable and clear.

Tool Count3/5

With 33 tools, the server is on the heavier side. The server covers a broad scope (data queries, prediction markets, AI visibility, memory, utilities, subscriptions), which justifies many tools, but some feel redundant (e.g., three variants of ask_pipeworx, multiple polymarket edge tools). A more focused set (around 20-25) would be more typical for coherence.

Completeness4/5

The tool surface is extensive, covering factual Q&A via a universal router, entity profiles, comparisons, prediction market analysis, memory, subscriptions, AI visibility, and utilities. The meta-tool ask_pipeworx provides access to thousands of structured sources, so most data needs are addressable. Minor gaps include no direct tool for specific SEC filing retrieval beyond the meta-router, but this is covered indirectly.

Resources