Skip to main content
Glama

Fcc

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 provide readOnlyHint, openWorldHint, etc. Description adds valuable behavioral details: embedding model (BGE-base-en), cosine similarity, overlapping windows, 200K char cap with truncation flag, and offsets for verification.

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 thorough and well-structured but slightly verbose. Could be tightened without losing key information.

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 format (passages with offsets and similarity scores), limits, truncation behavior, and pairing with sibling. Covers all essential context.

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

Parameters5/5

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

Schema description coverage is 100%. Description adds extra context: max chars for text, natural-language query examples, and default limit (5) and range (1-20).

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, using specific examples like SEC 10-K body. It distinguishes from sibling tools by mentioning 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 Guidelines4/5

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

Explicitly states when to use ('when the record is too big to cram into the prompt') and how it pairs with a sibling. No explicit when-not-to-use, but context is clear.

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

All Fcc toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation3/5

Several tools cluster around the same core purpose: the three ask_pipeworx variants, the three census reverse-geocoders, and the six Polymarket analysis tools. Descriptions are detailed enough to disambiguate most choices, but ask_pipeworx_beta is currently identical to ask_pipeworx, creating genuine ambiguity. An agent could easily select the wrong tool in these overlapping families.

Naming Consistency3/5

Names mix verb-initial actions (ask_pipeworx, compare_entities, resolve_entity) with noun-initial compound names (census_block, entity_profile, polymarket_edges). The polymarket_* family is internally consistent, but the set as a whole lacks a uniform verb_noun convention. Single-word verbs like remember, recall, and forget further break the pattern.

Tool Count2/5

At 34 tools, this exceeds the 'too many' threshold of 25 and includes clear redundancy: ask_pipeworx_beta duplicates ask_pipeworx, county_for_point is a thin wrapper over the same service as census_area/census_block, and scan_competitor_ai_presence just loops ai_visibility_check. The broad scope does not justify this many tools, and the set would be better split into focused servers.

Completeness4/5

Each sub-domain has solid lifecycle coverage: memory has remember/recall/forget, subscriptions have subscribe/list/unsubscribe/recent_alerts, and company research has resolve_entity/entity_profile/compare_entities/recent_changes. Minor gaps exist (e.g., no direct pipeworx:// citation-fetching tool), but no critical dead ends that would cause agent failures.

Resources