Skip to main content
Glama

Data Gr

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)

TDQS

A4.2/5.0
Behavior4/5

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

Annotations indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value by explaining scoping to identifier (anonymous IP, BYO key hash, or account ID), which is not in annotations. No contradictions.

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 concise, with three short sentences that front-load the main action and include essential details about usage and pairing. No extraneous content.

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?

Given the tool's simplicity (one optional param, no output schema), the description covers purpose, usage, scoping, and pairing. It lacks a hint about return format (e.g., list of keys or single value), but this is minor.

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?

Schema coverage is 100% with one parameter 'key' described. The description essentially restates the schema's guidance to omit the key for listing. No additional semantic meaning is added 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 clearly states the tool retrieves values saved via 'remember' or lists all keys, with a specific verb and resource. It distinguishes from siblings 'remember' and 'forget' by explicitly naming them.

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?

The description specifies when to use the tool: to look up context stored earlier, avoiding re-derivation. It also explains how to list all keys by omitting the argument. However, it does not explicitly mention when not to use or alternatives beyond the paired tools.

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

All Data Gr toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there is some overlap among the meta-querying tools like ask_pipeworx, ask_pipeworx_grounded, deep_research, and discover_tools, which could cause confusion for an agent deciding which to use.

Naming Consistency3/5

Tool names use a mix of verb_noun and noun patterns, with snake_case throughout but no single consistent structure (e.g., ask_pipeworx vs. bet_research vs. dataset). The naming is readable but not uniform.

Tool Count4/5

At 33 tools, the count is on the higher side but justifiable given the broad scope of the Pipeworx platform, covering data querying, entity analysis, prediction markets, memory, subscriptions, and feedback.

Completeness4/5

The toolset covers a wide range of data sources and operations, including querying, entity profiling, comparisons, prediction market analysis, and monitoring. Minor gaps exist (e.g., no direct SEC filing viewer), but the meta-tools handle these adequately.

Resources