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Glama

Fas

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. 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 destructiveHint=false, so the safety profile is clear. The description adds scope (anonymous IP, BYO key hash, account ID) which is valuable context 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?

Three concise sentences, each earning its place: first states purpose, second explains use case, third adds scope and pairing. No redundancy or waste.

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 no output schema, the description covers all essential aspects: purpose, usage, scope, and relation to siblings. Annotations provide safety profile. Complete.

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% (key property described). The description adds that omitting key lists all keys, which clarifies the optionality and behavior, adding value 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 specifies the verb 'retrieve' and 'list', the resource 'value previously saved', and explicitly distinguishes from siblings 'remember' and 'forget'. It clearly states the dual behavior based on presence of key argument.

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?

It says 'Use to look up context the agent stored earlier... without re-deriving it from scratch' and mentions pairing with remember/forget. It doesn't explicitly list when not to use, but provides clear intent and scoping details.

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
Disambiguation4/5

Most tools have clearly distinct purposes (e.g., ask_pipeworx vs. deep_research vs. validate_claim). However, a few pairs like ask_pipeworx_beta vs. ask_pipeworx and validate_claim vs. ask_pipeworx_grounded have overlapping roles, even though descriptions do differentiate them.

Naming Consistency4/5

All tool names use snake_case consistently, and many follow a verb_noun pattern (ask_pipeworx, compare_entities, subscribe). Some exceptions like entity_profile, recent_alerts, and pipeworx_trending break the strict verb_noun pattern but remain readable and stylistically uniform.

Tool Count3/5

With 35 tools, this is above the typical 'well-scoped' range and exceeds the 25-tool threshold for heavy servers. However, the server covers a very broad domain (financial data, prediction markets, agriculture, AI visibility, memory, subscriptions), which partially justifies the count, but it still feels bloated.

Completeness4/5

The tool surface covers all major workflows: data querying (ask_pipeworx, deep_research), entity resolution and comparison, prediction-market analysis, agricultural data (FAS tools), memory (remember/recall/forget), and subscription management (subscribe/unsubscribe/list). The only minor gap is lack of direct write/update operations for external data, but that's not expected for a read-heavy platform.

Resources