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Glama

Data Dc

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

A4.4/5.0
Behavior4/5

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

Annotations provide readOnlyHint, idempotentHint, destructiveHint. Description adds behavior for omitting key (list all) and scoping, which goes beyond annotations without contradicting 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?

Three sentences, front-loaded with main action, efficient and no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple retrieval tool with no output schema, description covers purpose, usage, scoping, and pairing with siblings. Could mention error handling (e.g., missing key) but sufficient as is.

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% with description for the key parameter. Description adds value by explicitly stating that omitting key lists all keys, which is not in 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?

Description clearly states the tool retrieves a value saved via remember, or lists all saved keys if key is omitted. This specific verb-resource pairing distinguishes it from sibling tools remember (save) and forget (delete).

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?

Explicitly describes when to use (look up context stored earlier) and pairs with remember and forget. Scoping to identifier is noted. No explicit when-not but context is sufficiently clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Data Dc toolsInspect
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TDQS

A4.1/5.0
Disambiguation3/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical; multiple Polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) share similar edge-detection and arbitrage goals, creating potential confusion for an agent.

Naming Consistency4/5

Most tool names follow a consistent verb_noun pattern using underscores (e.g., ask_pipeworx, compare_entities, resolve_entity). There are minor deviations like generate_llms_txt and scan_competitor_ai_presence, but overall the naming is predictable and clear.

Tool Count3/5

With 34 tools, the server is on the heavier side but still justified given its broad scope (data querying, entity profiles, monitoring, research, etc.). The count feels slightly high, but each tool serves a specific purpose; however, some consolidation could reduce redundancy.

Completeness4/5

The tool set covers a wide range of tasks: data lookup, entity profiling, comparison, monitoring, memory, research, and claim verification. Minor gaps exist, such as lack of explicit data source listing or user preference management, but the core workflows are well-supported.

Resources