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Glama

agreements_list

List agreements. Filter by run_id, response_id, metric_id, or created_by.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
run_idNo
metric_idNo
created_byNo
response_idNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.1/5.0
Behavior3/5

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

No annotations are present, so the description carries the full burden. It states that the tool lists agreements and can filter by specific fields, which implies a read-only operation. However, it discloses no additional behavioral traits such as pagination, authorization requirements, or return format. It is minimally transparent but not misleading.

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 two short sentences, front-loaded with the core action. No unnecessary words or repetition. Every phrase adds value: the first sentence defines purpose, the second enumerates filter options.

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

Completeness3/5

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

For a simple list tool with four optional filters and no output schema, the description covers the basic purpose and filter parameters. However, it omits important context such as whether no filters returns all agreements, pagination behavior, and the shape of the response. It is adequate for a basic understanding but lacks completeness.

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 input schema has four parameters with no descriptions (0% coverage). The description adds meaning by explicitly identifying these parameters as filters, which is beyond the bare type information. It does not explain filter semantics (e.g., exact match, combination logic), but it compensates for the schema gap by labeling them as filtering criteria.

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 action 'List agreements' and identifies the resource. It distinguishes from sibling agreements_create by focusing on listing rather than creating. The filter fields are specified, making the purpose unambiguous.

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 provides clear context for when to use this tool: to list agreements with optional filters. It does not explicitly exclude alternatives or name sibling tools, but the listing vs. creating distinction is implicit. No when-not guidance is given, so it stops short of a perfect score.

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

B3/5.0
Disambiguation5/5

Each tool targets a distinct resource and action, with clear separation across agreements, datasets, judges, metrics, prompts, runs, tags, and usage. Even similar tools like datasets_create vs datasets_create_from_url and runs_generate vs runs_rerun are explicitly differentiated in their descriptions.

Naming Consistency5/5

The overwhelming majority of tools follow a consistent plural_resource_action snake_case pattern (e.g., datasets_create, metrics_update, runs_retry_failures). The only slight deviation is promptfoo_import, but it is still descriptive and does not break the overall predictability.

Tool Count1/5

With 54 tools, the server far exceeds the 25+ threshold considered too many, and approaches the 50+ extreme mismatch level. Even for a broad LLM evaluation platform, this count is excessive and likely to overwhelm agents, making tool selection more error-prone.

Completeness4/5

The toolset provides full CRUD for core resources (datasets, metrics, prompts, runs, tags) plus lifecycle operations like publish, generate, regrade, and retry. It also includes cross-cutting utilities (usage, import, provider credentials). Minor gaps exist, such as no update/delete for agreements and no cross-run response search, but these are non-essential for the primary workflows.