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Glama

Fcc Ecfs

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

A4.6/5.0
Behavior5/5

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

Annotations already declare idempotentHint=true and destructiveHint=false. The description adds critical behavioral details: scoping by identifier, persistence duration (24 hours for anonymous, permanent for authenticated), and that it is a key-value store. No contradictions.

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 with no wasted words. The purpose is front-loaded, and each sentence adds distinct value: purpose, when to use, storage behavior, and usage with siblings.

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?

The description covers purpose, usage guidance, behavior, and relationships with siblings. It does not explicitly describe the return value or success confirmation, which is a minor gap for a write operation. However, given the simple output (likely nothing or success), this is acceptable.

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%, so the schema already describes both parameters. The description adds value by providing examples of keys (subject_property, target_ticker) and clarifying that value accepts any text, which aids understanding beyond the schema.

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 uses a specific verb ('save', 'store') and defines the resource as key-value memory. It clearly distinguishes itself from sibling tools 'recall' and 'forget', and explains the scope across sessions.

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?

The description explicitly states when to use the tool ('when you discover something worth carrying forward') and mentions pairing with 'recall' and 'forget' for alternatives. It lacks explicit 'when not to use' but provides sufficient context.

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

A3.6/5.0
Disambiguation2/5

Many tools overlap in purpose, such as multiple query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and multiple prediction market analysis tools (bet_research, polymarket_arbitrage, polymarket_edges). An agent would struggle to select the appropriate tool due to ambiguous distinctions.

Naming Consistency3/5

Tool names are a mix of styles: some follow verb_noun (ask_pipeworx, forget), others use noun_verb (ecfs_docket_filings), and many are short or compound (bet_research, entity_profile). While the ecfs- prefix tools are consistent, the overall set lacks a uniform pattern.

Tool Count2/5

With 35 tools, the count is excessive for a server named after FCC ECFS, as only 4 tools are directly relevant to that domain. The inclusion of many general-purpose data tools makes the set feel bloated and misaligned with the server's apparent scope.

Completeness3/5

For the FCC ECFS domain, the tool set is incomplete (only 4 tools, lacking submission or deletion capabilities). However, as a general-purpose data query server, it covers many sources (SEC, FDA, patents, etc.). The name mismatch hurts the perceived completeness.

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