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Glama

lorem

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
Behavior4/5

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

Annotations already mark this as read-only, idempotent, and non-destructive. The description adds valuable behavioral context: scoping to the caller's identifier and the dual behavior (retrieve single key vs. list all keys). It doesn't mention edge-case behavior like key-not-found, but that's minor given the strong annotation coverage.

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, each earning its place: the first gives the core action, the second the use case and benefit, the third the scoping constraint, the fourth the sibling tool relationships. No filler, and the main purpose is front-loaded.

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 covers the core function, the list-all-keys variant, scoping, and how it interacts with remember/forget. Nothing essential is missing for an agent to invoke it correctly.

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% (the 'key' parameter is fully described as 'Memory key to retrieve (omit to list all keys)'). The description reinforces this and adds concrete examples of what keys might be (user's target ticker, address, research notes), offering more semantic guidance than the bare 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 clear, specific verb+resource: 'Retrieve a value previously saved via remember, or list all saved keys (omit the key argument).' It immediately distinguishes from related siblings (remember, forget) and gives concrete examples of what the tool retrieves.

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?

It explicitly states when to use this tool: 'Use to look up context the agent stored earlier... without re-deriving it from scratch.' It also names the companion tools for the full lifecycle: 'Pair with remember to save, forget to delete.' This provides clear context and differentiates from alternatives.

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
Disambiguation1/5

Multiple tools have nearly identical purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all handle natural-language data queries, with ask_pipeworx_beta explicitly duplicating ask_pipeworx. The prediction-market cluster (polymarket_edges, polymarket_arbitrage, bet_research) also heavily overlaps, and ai_visibility_check is a single-entity version of scan_competitor_ai_presence. An agent would frequently be unable to tell which tool to select.

Naming Consistency2/5

All names are snake_case, but the pattern is inconsistent: some are verb_noun (generate_llms_txt, resolve_entity), some are bare verbs (forget, recall, subscribe), and several are noun-first domain names (polymarket_edges, pipeworx_trending, entity_profile). There is no uniform verb convention, and the mix makes it hard to predict what a tool does from its name.

Tool Count2/5

At 33 tools, this is well above the 'heavy' threshold and includes several near-duplicates: three ask_pipeworx variants and six polymarket_* tools. While the underlying platform is broad, this meta-layer could be consolidated to 15-20 tools without losing capability.

Completeness4/5

The core data-query workflow is well covered: ask, deep research, entity profile, compare, validate, resolve ID, and search inside documents. The memory lifecycle (remember/recall/forget) and subscription lifecycle (subscribe/list/recent_alerts/unsubscribe) are also complete. However, the set includes unrelated utilities (generate_paragraphs, scan_dependency, generate_llms_txt) that don't belong to the main data domain, and there is no direct tool to execute a raw discovered tool by name.

Resources