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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

A4.7/5.0
Behavior5/5

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

The description adds important behavioral context beyond the annotations: 'Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours.' This discloses storage scope and retention behavior, which annotations do not cover. No contradiction with readOnlyHint=false or idempotentHint=true.

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?

The description is three concise sentences, front-loaded with the core purpose ('Save data...'), followed by usage guidance and behavioral details. Every sentence contributes valuable information with no redundancy.

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 two-parameter tool with no output schema and good annotations, the description covers purpose, usage, persistence, and pairing. It is fully sufficient for the agent to select and invoke the tool correctly within its context.

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% with clear descriptions for key and value. The description adds general context about key-value pairs and scoping but does not elaborate on the parameters themselves, so the baseline score of 3 applies.

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 opens with 'Save data the agent will need to reuse later,' a specific verb and resource. It explicitly differentiates from sibling tools by noting 'Pair with recall to retrieve later, forget to delete,' clearly positioning it against the recall and forget tools.

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?

It explicitly states when to use the tool: '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.' It also names the companion tools (recall, forget) as alternatives, providing clear contextual 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

A3.8/5.0
Disambiguation2/5

Many tools have overlapping purposes: ask_pipeworx and ask_pipeworx_beta are nearly identical, several prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread) cover similar territory, and research/verification tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim) blur together. The three sanctions tools are distinct, but the rest of the set creates frequent misselection risk.

Naming Consistency3/5

All tool names use snake_case, which is consistent, but the verb/noun ordering varies unpredictably (expectation vs entity_profile vs sanctions_screen vs scan_dependency). There is no dominant pattern like verb_noun, though names are readable.

Tool Count2/5

With 34 tools, the server is over-scoped, especially given its narrow 'Sanctions Screening' title. Many tools are duplicative (ask_pipeworx_beta duplicates ask_pipeworx; scan_competitor_ai_presence wraps ai_visibility_check), and the bulk are unrelated to the stated purpose. The count feels bloated.

Completeness3/5

The three sanctions tools (screen, entry, lists) cover the core read-only workflow well, but the server is mislabeled: most of the 34 tools are generic data/Pipeworx features unrelated to sanctions. For the broad data domain the set is fairly complete, but for the apparent 'Sanctions Screening' purpose it is both over- and under-scoped, with no bulk screening or list management.