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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

A4.3/5.0
Behavior4/5

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

Annotations declare idempotentHint=true and destructiveHint=false, but the description adds valuable context: key-value pairs scoped by identifier, persistent for authenticated users, and 24-hour retention for anonymous sessions. No contradictions with annotations.

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 action, each sentence adds essential information: purpose, usage context, persistence details, and companion tools. No fluff or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers core functionality and persistence context, but lacks mention of return values or success confirmation, which would be helpful for a write operation without an output schema. However, the tool is simple and the idempotent hint mitigates some uncertainty.

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 coverage is 100%, so baseline is 3. The description adds value by explaining the scoping mechanism (by your identifier) and naming conventions (e.g., subject_property), which go beyond the parameter descriptions.

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 starts with a clear verb+resource: 'Save data the agent will need to reuse later.' It distinguishes from sibling tools like recall and forget by explicitly mentioning pairing with them. The scope 'across this conversation or across sessions' adds specificity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description advises when to use: 'when you discover something worth carrying forward... so you don't have to look it up again.' It also mentions recall and forget as companion tools. However, it does not explicitly state when not to use (e.g., for transient data), leaving room for slight ambiguity.

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
Disambiguation3/5

Many tools overlap in purpose, especially the Pipeworx data query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and the Polymarket betting tools (bet_research, polymarket_arbitrage, etc.). Descriptions help differentiate, but an agent may still struggle to choose the correct one.

Naming Consistency4/5

Most tool names use snake_case and follow a verb_noun pattern (e.g., ask_pipeworx, resolve_entity, validate_claim). Some deviations exist (e.g., entity_profile, cheat_sheet) but overall the pattern is predictable.

Tool Count2/5

34 tools is excessive for a server named 'Owasp', as only 4 are directly OWASP-related (asvs_chapters, asvs_requirements, cheat_sheet, top10). The remaining 30 tools are for general data querying and betting, making the surface feel bloated and unfocused.

Completeness3/5

The OWASP domain is partially covered with ASVS requirements, cheat sheets, and Top 10 lists. However, notable gaps exist, such as the OWASP Testing Guide, Software Assurance Maturity Model (SAMM), or risk assessment tools. The Pipeworx tools are comprehensive but not relevant to OWASP.