Skip to main content
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. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds scoping to 'your identifier' and pairing intent, which adds behavioral context beyond 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 with no wasted words, front-loaded with the primary action. Concise yet fully informative.

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 retrieval tool with one optional parameter and good annotations, the description covers purpose, usage, scoping, and pairing completely.

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% with description for 'key'. The tool description adds examples of typical keys (ticker, address, notes) and clarifies omitting key lists all, enriching meaning beyond schema.

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 retrieves a saved value or lists keys, using specific verbs 'retrieve' and 'list'. It distinguishes from siblings by naming 'remember' and 'forget' tools.

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?

Provides clear context for use: 'to look up context the agent stored earlier... without re-deriving it from scratch.' While it doesn't explicitly state when not to use, the pairing with remember/forget gives good guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.1/5.0
Disambiguation2/5

Several tool families overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical entry points, while discover_tools/suggest_questions and entity_profile/compare_entities/recent_changes serve similar discovery/comparison roles. The polymarket_* cluster also has tightly related boundaries that require reading long descriptions to disambiguate, and version-related helper tools (go_mod, module, versions, version_info) add further confusion.

Naming Consistency3/5

Most tools follow a lowercase snake_case verb_noun pattern (list_subscriptions, resolve_entity, validate_claim), but there are notable deviations: bare verbs like remember/recall/forget, noun-only names like go_mod, module, versions, and version_info, and brand-prefixed names like ask_pipeworx and pipeworx_feedback. The naming is readable and generally predictable, yet the mix of conventions keeps it from being highly consistent.

Tool Count2/5

With 35 tools, the surface is heavy for what is ultimately a data-access and research server. Many tools are conveniences or meta-wrappers that could be consolidated (e.g., three ask_pipeworx variants, multiple polymarket scanners, several onboarding/discovery tools). While the breadth is intentional, 35 feels bloated rather than well-scoped.

Completeness4/5

The toolset covers a remarkably broad domain: universal data lookup, grounded fact-checking, entity resolution, company profiles, comparisons, prediction-market analysis, memory, subscriptions, and feedback. Minor gaps exist—subscriptions can be created/cancelled but not updated/paused, and there is no direct resolver for pipeworx:// citation URIs—but these are workable and core workflows have no dead ends.