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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 meaningful behavioral context beyond the annotations: memory is scoped by identifier, and persistence differs for authenticated vs anonymous sessions (permanent vs 24 hours). Annotations only indicate idempotence and non-destructiveness, so this extra context raises the score. It doesn't mention overwrite behavior, but the idempotentHint already implies safe repeated calls.

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 sentences, front-loaded with the core purpose, followed by usage context and persistence details. Every sentence earns its place—no redundant filler—and it is well-structured for quick scanning.

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 tool with two well-documented parameters, no output schema, and clear annotations, the description covers purpose, usage scenarios, persistence behavior, and related tools. It is complete enough for an agent to select and invoke the tool correctly without ambiguity.

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?

The schema already documents both parameters (key and value) with examples and full coverage, so the description's additional mention of 'key-value pair' adds little semantic value. The description's note about scoping by identifier is more about the storage context than the parameters themselves, 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 the tool's purpose with a specific verb and resource: 'Save data the agent will need to reuse later.' It distinguishes from siblings by giving examples (ticker, address, preference) and explicitly pairing with recall/forget, which are retrieval and deletion tools respectively.

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?

The description provides explicit usage guidance: 'Use when you discover something worth carrying forward... so you don't have to look it up again.' It also names the related tools (recall, forget) to indicate complementary alternatives, making it clear when to use this tool versus the siblings.

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

B3.1/5.0
Disambiguation2/5

Many tools have overlapping research purposes (ask_pipeworx, deep_research, ask_pipeworx_grounded) and multiple bet-related tools (bet_research, polymarket_arbitrage, polymarket_edges). Reactome-specific tools are few but mixed in with unrelated tools, causing ambiguity.

Naming Consistency2/5

Tool names are inconsistent: some snake_case (ask_pipeworx, deep_research), some camelCase (generate_llms_txt, list_subscriptions), and some mixed (pipeworx_feedback, poly market_arbitrage). No uniform pattern.

Tool Count2/5

35 tools is excessive for a Reactome server, as only a handful are Reactome-specific. Many tools are unrelated (e.g., bet_research, compare_entities), making the tool count feel bloated and unfocused.

Completeness2/5

The Reactome-specific tools cover basic pathway lookups but miss key operations like reactions, complexes, or advanced queries. The server's completeness for the Reactome domain is poor, diluted by many non-Reactome tools.