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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

A4.9/5.0
Behavior5/5

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

Adds significant behavioral context beyond annotations: storage is key-value scoped by identifier, persistent for authenticated users, 24-hour retention for anonymous sessions. Annotations only provide idempotentHint=true; description enriches with lifecycle and scoping details.

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?

Compact and well-structured: first sentence states purpose, second sentence gives usage context, third explains storage behavior, fourth ties to siblings. No filler, every sentence earns its place.

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?

Fully equips the agent: describes purpose, when to use, parameter semantics, storage behavior, and sibling relationships. No gaps given the tool's simplicity and existing annotations.

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 already covers both parameters with examples (100% coverage). Description reinforces that key is a label and value is arbitrary text, and explains the key-value pair concept. Slightly redundant with schema but adds contextual value.

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?

Explicitly states 'Save data the agent will need to reuse later', with a specific verb and resource. Distinguishes from sibling tools recall and forget by name, making its unique role clear.

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?

Directly states when to use ('when you discover something worth carrying forward') and provides explicit alternatives (recall to retrieve, forget to delete). Also clarifies session vs. persistent memory, guiding agent decision-making.

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
Disambiguation2/5

Multiple tools occupy the same functional space: ask_pipeworx, ask_pipeworx_beta, ask_ipeworx_grounded, and deep_research all answer questions; disover_tools, suggest_questions, and pipeworx_trending all serve discovery; and five polymarket_* tools plus bet_research overlap heavily on prediction-market opportunity detection. The descriptions are detailed, but the boundaries are subtle enough that an agent can easily pick the wrong tool.

Naming Consistency4/5

The set is uniformly lowercase snake_case and uses recognizable prefixes such as ask_pipeworx, polymarket_, pipeworx_, and get_, which makes the naming fairly predictable. It is not a strict verb_noun convention — some names are noun phrases like entity_profile or ai_visility_check — but the overall style is consistent.

Tool Count2/5

With 36 tools, the surface is far heavier than the 'Congress' name suggests: only five tools are actually about congressional data, while the rest are general research, memory, subscription, and meta utilities. Many of these overlap, so the count feels bloated rather than well-scoped.

Completeness3/5

For Congress-specific work, the core needs are covered: search bills, get bill details, list members, and retrieve recent votes. However, deeper legislative operations like member voting records, committee actions, and amendments are missing, and the surrounding Pipeworx tools do nothing to close that gap. As a general data-research platform it is broad, but its actual 'Congress' identity feels incompletely realized.