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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. First observed

TDQS

A5/5.0
Behavior5/5

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

The description adds behavioral context beyond annotations, including scoping to identifier (anonymous IP, BYO key hash, account ID) and dual behavior (list vs. retrieve). It aligns with readOnlyHint, idempotentHint, and destructiveHint annotations without 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?

The description is concise (two sentences) yet comprehensive, front-loading the core action and proceeding to examples, scoping, and related tools. Every sentence adds value with no 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?

Given the low complexity (one optional parameter, no output schema), the description fully covers purpose, usage, parameters, scoping, and relationships to siblings. No gaps remain for effective agent invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'key' is fully covered by schema (100% coverage). The description adds critical meaning: omitting the key lists all saved keys, providing behavior beyond schema alone. Examples further clarify usage.

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: retrieve a saved value by key or list all keys when omitted. It distinguishes itself from siblings 'remember' and 'forget' by specifying the operation and pairing guidance.

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 with concrete examples (target ticker, address, research notes) and explains when to use it (look up stored context) and when not to (e.g., use forget for deletion). It also mentions scoping and pairing with 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

A3.9/5.0
Disambiguation2/5

Multiple research/query entry points overlap heavily: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded and deep_research sit on the same routing core, and validate_claim/bet_research/entity_profile all wrap lookup-and-analyze behavior. The detailed descriptions help within specialized clusters, but the central ask_pipeworx family alone creates real selection ambiguity.

Naming Consistency3/5

All names are lower_snake_case and several families are consistent (ask_pipeworx_*, polymarket_*, remember/recall/forget), but the overall set mixes bare verbs, nouns, and verb_noun composites with no global pattern (disease, metadata, query, entity_profile, generate_llms_txt, validate_claim). It is readable but not predictable across the full 34-tool surface.

Tool Count2/5

34 tools is over the 25+ threshold and the set bundles several distinct domains—disease ontology, Pipeworx data access, prediction markets, AI visibility, npm scanning, memory, and subscriptions—into one server. Each subfamily may be justified, but the combined surface is heavy and makes tool selection harder than the underlying tasks require.

Completeness4/5

The disease domain has query/disease/metadata for search-and-fetch read coverage, and the broader research side has lookup, grounded verification, deep research, entity profiles, comparisons, subscriptions, and memory lifecycle tools. Minor gaps exist (no direct tool to fetch pipeworx:// citation URIs, no disease browsing/pagination), but these are workable rather than blocking.