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Glama

Gov Bids

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

A4.2/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations: scoped by identifier, persistent memory for authenticated users vs. 24-hour retention for anonymous sessions. Annotations already indicate idempotent and non-destructive, and description aligns with 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is moderately long but well-structured: each sentence adds value (purpose, usage, storage details, pairing). Not overly verbose, though slightly redundant with schema for parameter names.

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

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers key aspects (persistence, pairing), but does not mention return behavior or what happens on key collision. Given no output schema, a bit more detail would improve completeness.

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% with clear parameter descriptions. The description provides examples (e.g., 'subject_property') but adds no new syntax or format beyond schema. Baseline score of 3 is appropriate.

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's purpose: 'Save data the agent will need to reuse later.' It specifies the scope (across conversation or sessions) and gives concrete examples. It distinguishes itself from sibling tools by mentioning pairing with recall and forget.

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: 'when you discover something worth carrying forward' and 'so you don't have to look it up again.' It also pairs with recall/forget, providing context, but lacks explicit 'when not to use' guidance.

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

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but the multiple Pipeworx query variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and multiple Polymarket tools could cause some confusion. However, descriptions are detailed enough to differentiate them.

Naming Consistency3/5

Naming patterns are mixed: some tools use verb_noun (list_subscriptions, open_bids_search), others use noun_phrase (entity_profile, bet_research), and cases are inconsistent (snake_case vs underscores). While not chaotic, the lack of a strong consistent pattern reduces coherence.

Tool Count2/5

33 tools is high, and the server's name 'Gov Bids' suggests a focused scope, but most tools are unrelated (AI visibility, npm packages, general data queries). The tool count feels excessive for a focused server, and the scope is too broad.

Completeness4/5

For a general-purpose data server, the tool set is quite comprehensive across multiple domains (SEC, FDA, economics, prediction markets, etc.). Minor gaps exist (e.g., government contracts beyond bids), but overall coverage is strong.

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