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Glama

Recall

recall
Read-onlyIdempotent

Retrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous IP, BYO key hash, or account ID). Pair with remember to save, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNoMemory key to retrieve (omit to list all keys)

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
      +  {
      +    "key": "user_research_topic"
      +  },
      +  {}
      +]
  2. Added

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds valuable context: the tool is scoped to the agent's identifier (anonymous IP, BYO key hash, or account ID) and that omitting the key lists all saved keys, which goes beyond the 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?

The description is two sentences, front-loaded with the primary action, and every sentence earns its place. It efficiently conveys purpose, usage, scoping, and related tools without 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 tool with one optional parameter, no output schema, and strong annotations, the description is complete. It covers the two modes of operation, scoping, and complementary tools, providing sufficient information for an agent to select and invoke it correctly.

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 meaningful context by giving examples of keys (target ticker, address, research notes) and clarifying the dual behavior when the key is omitted, enhancing the schema's description.

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 a specific verb+resource: 'Retrieve a value previously saved via remember, or list all saved keys'. It distinguishes itself from siblings by referencing remember and forget, making its purpose unambiguous.

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 gives clear context: use it to look up previously stored context without re-deriving it. It also mentions pairing with remember and forget, but does not explicitly state when not to use it or name alternative tools beyond these companions.

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

A4/5.0
Disambiguation3/5

Most tools are clearly distinct, but the three ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are nearly identical, with the beta explicitly matching the stable version. Additionally, ai_visibility_check and scan_competitor_ai_presence overlap in purpose, creating some selection ambiguity.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern (e.g., get_joke, search_jokes, resolve_entity, validate_claim). Even the more noun-like names like entity_profile and pipeworx_feedback fit the readable convention. No mixed camelCase or erratic verb usage.

Tool Count2/5

With 34 tools, the count is far above the typical well-scoped range of 3-15. The server is named 'dadjokes' but only 3 tools actually serve that purpose; the remaining 31 are an unrelated Pipeworx research and prediction-market platform, making the scope feel bloated and misaligned.

Completeness3/5

For the nominal dad-joke domain, the surface is complete (get, random, search). However, the intended domain is ambiguous given the massive unrelated toolset; it's unclear what a user should expect the server to cover, and the analysis/prediction tools lack obvious CRUD or management counterparts.