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Glama

Data Denver

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. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description adds value by explaining scoping to the agent's identifier and the dual behavior (retrieve by key or list all). 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?

Two concise sentences with the core action front-loaded. No wasted words; each sentence contributes purpose, context, and pairing guidance.

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 read tool with one optional parameter and no output schema, the description covers behavior, scoping, and relationships. Annotations handle safety. 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?

The single optional parameter 'key' is described in the schema. The description adds that omitting key lists all keys, which is meaningful context beyond the schema's type definition.

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 value saved via remember or lists all saved keys, using a specific verb and resource. It distinguishes from sibling tools remember and forget by describing complementary actions.

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?

Provides explicit context on when to use (to look up stored context without re-deriving) and scoping (by identifier). Mentions pairing with remember and forget, guiding appropriate usage.

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
Disambiguation3/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical (beta is currently exactly the same), and scan_competitor_ai_presence is a multi-entity wrapper around ai_visibility_check. The detailed descriptions help, but an agent could easily pick the wrong variant when a simple lookup is needed.

Naming Consistency3/5

Tool names mix verb-first patterns (ask_pipeworx, resolve_entity, scan_dependency, validate_claim) with noun-first patterns (denver_layers, entity_profile, recent_changes, pipeworx_trending, polymarket_edges). There are clear families (ask_pipeworx_*, denver_*, polymarket_*, pipeworx_*) but no single consistent verb_noun convention across the set.

Tool Count2/5

34 tools is a large surface for one server, exceeding the 25-tool threshold where coherence starts to degrade. Many tools are meta-routers or near-duplicates (ask_pipeworx family), and the mix of general data access, Denver-specific queries, prediction-market analysis, memory, and subscriptions feels sprawling rather than tightly scoped.

Completeness4/5

The tool surface covers the apparent domain well: universal data lookup, grounded evidence, deep research, entity resolution, company profiles, comparisons, claim validation, AI visibility, dependency scanning, prediction-market analysis, subscriptions, and memory. Minor gaps exist (e.g., no direct update tool for subscriptions, no way to inspect the full 5,798-tool catalog locally without routing through ask_pipeworx), but agents can accomplish most workflows without dead ends.

Resources