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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. Added

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare idempotentHint=true and readOnlyHint=false. The description adds meaningful behavioral context beyond annotations: key-value storage scoped by identifier, persistence differences for authenticated vs anonymous users (24 hours), and the pairing pattern with recall/forget. No annotation contradiction.

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, front-loaded with the core purpose in the first sentence. Each subsequent sentence adds needed context: usage triggers, storage mechanism, retention policy, and companion tools. No redundant information.

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?

Covers the tool's purpose, when to use, storage semantics, persistence behavior, and related operations. Given the tool's simplicity (two parameters, no output schema), it is fully self-contained for an agent to invoke correctly.

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?

Input schema provides complete descriptions for both key and value (100% coverage), including examples. The description only refers to 'key-value pair' without adding new constraints or format details, so baseline 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,' clearly stating the verb (save) and resource (data for later reuse). It distinguishes itself from sibling tools recall (retrieve) and forget (delete) by explicitly naming them, and clarifies cross-session persistence.

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?

Provides explicit guidance: 'Use when you discover something worth carrying forward' with concrete examples (resolved ticker, target address, user preference, research subject). It also specifies the complementary tools: 'Pair with recall to retrieve later, forget to delete.'

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

A4.1/5.0
Disambiguation5/5

Every tool has a clearly distinct purpose, from Wikipedia page views to AI visibility checks, entity resolution, and Polymarket betting. No two tools appear overlapping in functionality; descriptions further clarify each tool's unique role.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with clear verb_noun structure (e.g., get_article_views, subscribe, resolve_entity). No mixing of conventions, and names are descriptive enough to infer purpose.

Tool Count2/5

With 33 tools, the server is overloaded for its name 'wikiviews', which suggests a focused Wikipedia views tool. The set includes unrelated functionality like Polymarket arbitrage, memory storage, and Pipeworx data queries, making the scope feel excessive.

Completeness2/5

For the core domain of Wikipedia views, only 3 tools exist (get_article_views, get_project_views, get_top_articles), missing basic operations like list_articles_per_day. The unrelated tools are extensive, but the server's stated purpose is poorly served.