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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 and idempotentHint=true. The description adds context about scoping to an identifier and pairing with siblings, but does not elaborate on auth or side effects beyond annotations. No 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, front-loaded with the action, and uses minimal sentences to cover all necessary information without excess.

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 tool with one optional parameter and no output schema, the description fully covers behavior, usage, scoping, and relationships to siblings. No gaps.

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?

Input schema covers the 'key' parameter with 100% description coverage. The description adds nuance: omitting key lists all keys, and key should correspond to a saved value, adding meaning beyond the 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 'Retrieve a value previously saved via remember, or list all saved keys (omit the key argument)', using a specific verb+resource structure. It distinguishes from siblings like 'remember' and 'forget'.

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 explains when to use ('to look up context the agent stored earlier... without re-deriving it from scratch') and provides pairing guidance with 'remember' and 'forget'. It does not explicitly state when not to use, but the context is clear.

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

A3.8/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx and ask_pipeworx_beta are described as currently identical, and deep_research/ask_pipeworx plus the many polymarket_* tools have adjacent intents that are easy to confuse. scan_competitor_ai_presence also directly wraps ai_visibility_check, so an agent can easily select the wrong tool for the same task.

Naming Consistency3/5

Names are consistently snake_case, but the convention is mixed: some are verb-first (query_subgraph, introspect_schema, validate_claim), some are noun-first (entity_profile, polymarket_edges, pipeworx_trending), and some are bare verbs (remember, recall, forget). The repeated prefixes help, but there is no single predictable naming pattern across the set.

Tool Count2/5

With 33 tools, this exceeds a reasonable single-server footprint, and the scope sprawls across data routing, prediction markets, AI visibility, npm dependencies, llms.txt generation, memory, and subscriptions. Many tools feel like separate mini-applications rather than one coherent toolkit for The Graph.

Completeness3/5

The Pipeworx data-research and prediction-market side is well covered with lookup, grounded answers, research, comparison, profiles, claims, subscriptions, and memory. However, the server's apparent namesake, The Graph, is thin: only query_subgraph and introspect_schema exist, with no subgraph discovery, status, or management tools.