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Glama

jikan

by io.github.pipeworx-io

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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is clear. The description adds valuable behavioral context about scoping to anonymous IP, BYO key hash, or account ID, and that omitting the key lists all saved keys. No contradiction with 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?

Two well-structured sentences: first states the core action and the alternative list behavior, second gives concrete usage examples and pairing guidance. Every sentence earns its place with no redundancy or fluff.

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 simple one-optional-parameter tool, strong annotations, and complete schema, the description fully covers usage, scoping, and relationship to remember/forget. No output schema is needed for a retrieval tool, and nothing important is missing.

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 key parameter already has a clear description ('Memory key to retrieve (omit to list all keys)'). The description repeats this and adds scoping context, but doesn't introduce new parameter-specific meaning beyond the schema, 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 tool retrieves a previously saved value via remember, and also supports listing all keys when omitted. It distinguishes itself from sibling tools like remember and forget by explicitly pairing with them, and specifies the resource (saved memory values).

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 concrete usage context: looking up previously stored context like target ticker, address, or research notes, and notes scoping to identifier. It mentions pairing with remember/forget but doesn't explicitly state when not to use it or name alternatives, though the context is strong.

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
Disambiguation2/5

Many tools serve overlapping purposes (ask_pipeworx, ask_pipeworx_grounded, deep_research, bet_research, compare_entities, entity_profile, recent_changes, validate_claim) all querying Pipeworx data with similar outcomes. An agent would struggle to choose correctly without deep understanding of nuanced differences.

Naming Consistency2/5

Naming conventions are mixed: snake_case (ask_pipeworx, get_anime), camelCase (generate_llms_txt, pipeworx_feedback), and compound names (polymarket_arbitrage, ai_visibility_check). No consistent pattern across the tool set.

Tool Count2/5

34 tools is excessive for a single server. Many are meta-tools (discover_tools, suggest_questions) or narrowly focused (pipeworx_trending, scan_dependency). The server tries to cover too many domains (anime, financial data, predictions, memory) in one surface.

Completeness3/5

The anime tools (search, get, top) form a reasonable read-only surface. The Pipeworx query tools are comprehensive but lack obvious data management tools (e.g., listing sources, managing credentials). Memory tools (remember/recall/forget) are isolated. Overall, gaps exist but core workflows are covered.

Resources