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Glama

Openligadb

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

A4.7/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: scoping to identifier (anonymous IP, BYO key hash, account ID) and pairing with remember/forget. 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?

Three sentences, no wasted words. First sentence states core function, second gives examples, third adds scoping and pairing. Front-loaded and efficient.

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?

Given one optional parameter, no output schema, and thorough annotations, the description fully covers what an agent needs: purpose, usage, scoping, and relationships with siblings.

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 a description for the key parameter. The description adds examples of use cases (user's target ticker, address, prior research notes), providing meaning beyond the schema. Baseline 3, plus 1 for added context.

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 keys when key is omitted. It specifies the verb (retrieve/list) and resource (saved values/keys), and distinguishes from siblings remember and forget.

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 explains when to use (look up context stored earlier) and how to use (omit key to list all). It references sibling tools remember and forget for lifecycle, but does not explicitly state when not to use or provide alternative tools.

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

All Openligadb toolsInspect
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TDQS

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, with detailed descriptions differentiating similar tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research. However, some overlap exists between ask_pipeworx and ask_pipeworx_beta, as both serve as universal routers with only minor routing improvements.

Naming Consistency2/5

Tool names follow inconsistent patterns: some use snake_case (ask_pipeworx, entity_profile), others use lowercase single words (forget, recall), and some use camelCase (bet_research, deep_research). This mix of conventions makes the naming scheme unpredictable.

Tool Count3/5

With 35 tools, the server covers a wide range of data sources, but the count feels slightly heavy for the apparent scope. Several tools serve meta-purposes (discover_tools, suggest_questions) or specialized functions (polymarket_arbitrage), adding to the complexity.

Completeness4/5

The tool set offers comprehensive coverage for financial, economic, drug, and news data, including comparison and grounding capabilities. However, the football-related tools are limited to German leagues, leaving a minor gap for other sports or regions.

Resources