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Glama

Ecfr

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

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds valuable technical details: BGE-base-en embeddings, cosine over 500-char windows, 200K char cap with truncation, and offset for verification. No contradiction.

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 efficiently cover purpose, use case, workflow pairing, and technical constraints. No redundant information. Well-structured and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

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 type (passages with offsets and scores) and limitations (cap, truncation). Slight lack of detail on passage structure, but adequate with annotations and sibling context.

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 coverage is 100%, so baseline 3. Description repeats max chars for text and gives examples for query, but adds minimal extra meaning beyond the schema's own descriptions.

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 it performs semantic search inside a fetched record, returning relevant passages with offsets. Distinguishes from sibling ask_pipeworx_grounded by specifying it acts on a single record's text rather than grounding over full documents.

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 advises 'Use when the record is too big to cram into the prompt' and pairs with ask_pipeworx_grounded. Lacks explicit when-not-to-use or full list of alternatives, 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 Ecfr toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates (beta is currently identical), and ai_visibility_check vs scan_competitor_ai_presence plus deep_research vs ask_pipeworx create real selection ambiguity. Some clusters like the memory trio and CFR read tools are distinct, but the overall set is confusing.

Naming Consistency3/5

Most tools use snake_case and many follow a verb_noun pattern (search_regulations, generate_llms_txt, validate_claim), but noun-first names (entity_profile, title_structure, ai_visibility_check) and prefix families (polymarket_*, pipeworx_*) break the pattern. The conventions are mixed but still readable and mostly predictable.

Tool Count2/5

35 tools is heavy for any single server, and the bulk of them (Polymarket betting, memory, AI visibility, npm scanning, subscriptions) are unrelated to the server's 'Ecfr' name, which suggests a narrow regulatory focus. This is a kitchen-sink scope, making the count feel bloated rather than well-scoped.

Completeness3/5

The eCFR-specific surface is thin — list_titles, search_regulations, get_section_text, and title_structure cover basic read/search but lack version history, update tracking, or agency-level navigation. Other mini-domains (data lookup, polymarket, subscriptions, memory) are individually fairly complete, but the absence of a unified purpose leaves clear gaps overall.

Resources