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Glama

ureweries

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

A4.7/5.0
Behavior5/5

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

Annotations declare readOnlyHint, idempotentHint, and destructiveHint false, but the description adds substantive context: scoping to the agent's identifier (anonymous IP, BYO key hash, or account ID) and the behavior of listing all keys when key is omitted. This goes beyond what annotations provide and does not contradict them.

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 only two sentences, front-loaded with the primary action, and every clause adds value. It avoids redundancy and clearly separates behavior, usage, and scoping.

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 tool with one optional parameter and no output schema, the description is complete. It covers purpose, usage context, scoping, and relationships to sibling tools, leaving no important gaps for an agent to use it correctly.

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?

The input schema covers the single parameter 'key' with 100% schema description coverage, so the baseline is 3. The description adds minor context by explaining that the key is saved via remember and that omitting it lists all keys, but it does not add new syntax or format details 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 opens with a specific verb+resource: 'Retrieve a value previously saved via remember, or list all saved keys (omit the key argument).' It clearly distinguishes the tool from siblings like remember and forget by naming them and stating its complementary role.

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 explicitly states when to use the tool: '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.' It also references companion tools (remember, forget), giving clear alternatives and a paired workflow.

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
Disambiguation4/5

Most tools have clear distinct purposes, but ask_pipeworx, ask_pipeworx_grounded, and deep_research overlap in querying Pipeworx data sources. Entity_profile and recent_changes also share some coverage. Overall, agents can differentiate, but a few pairs may cause confusion.

Naming Consistency3/5

Tool names use snake_case for multi-word (e.g., ai_visibility_check) but also single-word verbs (forget, recall, remember). The pattern is not uniform: some are verb_noun, some are just noun or verb. Mixed conventions reduce predictability.

Tool Count3/5

At 33 tools, the set is on the high side for an MCP server. The server covers a broad data-query domain, which justifies many specialized tools, but the count is borderline heavy and includes several meta-tools (discover_tools, suggest_questions). Could be streamlined without losing core functionality.

Completeness4/5

The tool surface is comprehensive for data retrieval across financial, economic, pharmaceutical, real estate, weather, and prediction markets. It includes both single-lookup and comparative tools, plus monitoring via subscriptions. Minor gaps exist (e.g., no direct update/delete for user memory beyond forget), but the core domain is well-covered.

Resources