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Glama

Washington State Open Data

Forget

forget
DestructiveIdempotent

Delete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key to delete

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already indicate destructiveHint=true and idempotentHint=true. The description adds context about clearing sensitive data and pairing, but doesn't detail side effects or reversibility. With annotations present, the added value is moderate.

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 consists of two efficient sentences: first states the action, second provides usage guidance. It is front-loaded with no wasted words.

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

Completeness4/5

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

For a simple delete tool with one required parameter and no output schema, the description is fairly complete. It covers purpose, usage, and pairing with sibling tools, leaving little 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?

Schema coverage is 100% and the parameter 'key' is described as 'Memory key to delete' in the schema. The description does not add further details beyond 'by key', 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 verb 'Delete' and resource 'previously stored memory'. It explicitly distinguishes itself from sibling tools 'remember' and 'recall' by pairing with them.

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 provides explicit use cases: 'when context is stale, the task is done, or you want to clear sensitive data'. It also mentions pairing with remember and recall, implying alternatives, though it does not explicitly state when not to use.

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

A3.6/5.0
Disambiguation4/5

Most tools have distinct purposes with detailed descriptions, but there is overlap between ask_pipeworx, ask_pipeworx_grounded, and deep_research, all serving data retrieval. Also, prediction market tools (e.g., bet_research, polymarket_arbitrage) are closely related, causing potential ambiguity.

Naming Consistency2/5

Naming conventions are inconsistent: mostly snake_case (ask_pipeworx, entity_profile) but includes camelCase (ai_visibility_check). No clear pattern, mixing verb_noun and other structures.

Tool Count1/5

33 tools is excessive for a server named 'Washington State Open Data'; only 3 tools (datasets, metadata, query) are directly relevant, while the rest are unrelated Pipeworx/Polymarket tools. The count does not match the server's stated scope.

Completeness1/5

The tool set severely misaligns with the server name: it covers general data and prediction markets rather than Washington State Open Data. Only basic query and metadata tools exist for the stated domain, leaving many common dataset operations (e.g., CRUD) missing and providing entirely irrelevant functionality.