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Glama

Ecfr

by io.github.pipeworx-io

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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations indicate idempotentHint=true, destructiveHint=false. The description adds scoping by identifier, persistence for authenticated users vs. 24-hour retention for anonymous sessions, going beyond annotations to explain behavior.

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?

The description is well-structured, starting with purpose, then when-to-use, then details. However, it could be slightly more concise without losing clarity, but overall effective.

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 the tool's simplicity (2 params, no output schema), the description covers purpose, usage, behavior, persistence, and privacy implications fully. No gaps.

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% with parameter descriptions. The description reinforces the key-value pair nature and provides usage examples in the body, adding context beyond schema, though the schema is already detailed.

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 the tool saves data for reuse across conversations/sessions. It uses a specific verb ('save') and resource ('data'), and distinguishes itself from siblings like recall and forget by mentioning them as companions.

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?

The description explicitly says 'Use when you discover something worth carrying forward' and provides concrete examples (ticker, address, preference). It also mentions pairing with recall and forget for retrieval/deletion, giving clear context.

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