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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

A4.5/5.0
Behavior4/5

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

The description adds behavioral context beyond annotations: key-value storage scoped by your identifier, persistent memory for authenticated users, and 24-hour retention for anonymous sessions. This complements the idempotentHint with lifecycle details, though it doesn't describe return values or overwrite semantics.

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, each serving a distinct purpose: purpose, usage, storage mechanics, and companion tools. It's front-loaded with the verb and wastes no words.

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?

For a simple key-value store with full schema coverage and relevant annotations (idempotentHint), the description covers purpose, usage, persistence behavior, and relationships to recall/forget. No output schema exists, so not describing return values is acceptable.

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 description coverage is 100%, so the schema already documents both parameters. The description adds the key-value scoping context ('scoped by your identifier') but doesn't provide new syntax or format details, so baseline 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 a specific verb+resource: 'Save data the agent will need to reuse later.' It distinguishes itself from sibling tools by positioning as the store operation alongside 'recall to retrieve later, forget to delete,' making its role unambiguous.

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?

Explicitly says 'Use when you discover something worth carrying forward' and provides concrete examples (resolved ticker, target address, user preference). It also names alternatives (recall, forget), giving clear when-to-use vs. alternative-tool 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

A3.7/5.0
Disambiguation2/5

Multiple tools overlap heavily: ask_pipeworx and ask_pipeworx_beta are documented as currently identical, ask_pipeworx_grounded/deep_research route the same class of questions, and the six polymarket_* tools plus bet_research form a confusing cluster. Some clusters (Figshare fetch/search, memory, subscriptions) are distinct, but overall boundaries are frequently unclear.

Naming Consistency4/5

Names are uniformly lowercase snake_case and mostly follow a predictable verb_noun pattern (ask_pipeworx, list_subscriptions, resolve_entity, scan_dependency). The main inconsistency is the mix of bare-noun Figshare resource names (article, articles, collection, collections) with verb-phrase tool names.

Tool Count2/5

38 tools is well above the comfortable scope for a coherent server, especially one named Figshare. Most of the surface is unrelated to Figshare, bundling Pipeworx research, Polymarket analysis, memory, subscriptions, AI visibility, and npm checks into a single connection.

Completeness2/5

The Figshare read-side is reasonably covered (search, article metadata, files, collections, categories, licenses), but there are no create/update/delete or account/upload operations. The broader advertised surface is a grab bag with no clear domain boundary, and several subdomains are shallow while prediction markets are over-represented.