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Federal Register

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

A4.7/5.0
Behavior5/5

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

The description goes beyond annotations by detailing the embedding model (BGE-base-en), similarity metric (cosine), windowing (500-char overlapping), and character cap (200K chars with truncation). 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?

The description is a single, well-organized paragraph that front-loads purpose and efficiently adds details without redundancy.

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 explains the output (top-N passages with offsets and scores) and internal mechanics, making it complete for a complex 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?

Schemas cover 100% of parameters. The description adds value by providing query examples and reinforcing the character limit for text, slightly exceeding baseline.

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 a specific verb and resource. It distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded and the context of use.

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?

It explicitly says to use when the record is too large for the prompt and pairs with ask_pipeworx_grounded. While it doesn't state when not to use, it provides clear context with examples.

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

A3.5/5.0
Disambiguation1/5

Several tools are nearly indistinguishable: ask_pipeworx and ask_pipeworx_beta are explicitly identical in behavior, and ask_pipeworx_grounded overlaps heavily with them. Additionally, entity_profile, compare_entities, deep_research, and validate_claim all cover similar company/factual research territory, creating frequent selection ambiguity.

Naming Consistency2/5

Naming mixes multiple conventions: descriptive lowercase phrases (ai_visibility_check, compare_entities, valid claim) coexist with verb_noun (search_documents, recent_rules) and inconsistent underscores (ask_pipeworx vs ask_pipeworx_grounded, resolve_entity). There is no single recognizable pattern.

Tool Count1/5

The server is named 'Federal Register' but only 3 of 34 tools (search_documents, recent_rules, get_document) relate to that domain. The other 31 tools form a sprawling Pipeworx data and prediction-market suite, making the count extreme and inappropriate for the declared purpose.

Completeness2/5

For the stated Federal Register domain, the surface is minimal: search, recent listing, and single-document retrieval, with no docket browsing, full-text search within documents, or agency-specific navigation. The broader Pipeworx capability set is comprehensive but irrelevant to the server's name, leaving obvious gaps for the actual purpose.

Resources