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Glama

Campaign results & cost per usable reply

get_campaign_results
Read-only

The cumulative scorecard for a campaign: the funnel (posts discovered → kept → replies drafted → claimed → posted → approved), the approval rate, whether posted replies survived on-platform, the engagement they drew, total credits spent broken down by operation, and the cost per usable reply. Free, and it never scrapes — unlike the web report it costs nothing to poll. Use it to answer 'is this working and what am I paying for it?'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
campaign_idYes

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the description is not required to repeat safety. It adds value by disclosing that the tool is free and never scrapes, and that it costs nothing to poll, which informs an agent about rate-limit or cost implications. This goes beyond the annotations and provides useful behavioral context without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is thorough and well-structured, front-loading the funnel details and then the cost/usability question. It is slightly verbose but every sentence adds meaningful information—the funnel stages, metrics, cost breakdown, and usage context. It does not waste words on redundancy, so it earns a 4.

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 read-only reporting tool with a single parameter and no output schema, the description is quite complete. It enumerates all the data points the agent can expect, explains the cost model, and gives a usage scenario. The only missing piece is an explicit statement about the required campaign_id parameter and its format, but the rest is sufficient for an agent to call the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% (the schema has no descriptions for parameters), and the description does not mention the campaign_id parameter at all. Although the parameter is self-explanatory from its name, the description fails to compensate for the lack of schema documentation. The agent would have to infer that campaign_id refers to the target campaign ID, which is a gap given the low coverage.

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 states exactly what the tool does: it returns a cumulative scorecard for a campaign, enumerating the full funnel and metrics (approval rate, survival, engagement, credits spent, cost per usable reply). This is a specific verb+resource and is clearly distinct from the sibling tools, despite not naming them directly. The phrase 'unlike the web report' hints at an alternative without explicit differentiation, but the purpose is 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 a clear use case: 'Use it to answer "is this working and what am I paying for it?"' which guides when to call it. It also contrasts with the web report for cost and scraping behavior. However, it does not explicitly state when not to use it or name specific sibling alternatives, so it lacks explicit exclusions but is still helpful.

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

A4/5.0
Disambiguation4/5

Most tools map cleanly to a distinct resource and action: campaigns, posts, products, research, content spaces, and submissions. A few pairs are close—`create_campaign` vs `create_content_campaign` and `check_balance` vs `get_earnings`—but the detailed descriptions make the intended selection clear.

Naming Consistency4/5

The set largely follows a consistent `verb_noun` snake_case pattern (`list_campaigns`, `generate_posts`, `update_campaign`, `submit_participation`). Minor deviations keep it from a perfect score: `credit_history` lacks a verb, and `create_campaign` reads as generic next to the more specific `create_content_campaign`.

Tool Count3/5

30 tools is high and makes the server feel heavy, but the domain genuinely spans products, credits, campaigns, research, post discovery, content spaces, boosts, and participation rewards. The breadth mostly earns the count, though it sits on the wrong side of the typical 3–15 range.

Completeness4/5

The core end-to-end workflow is covered: create and configure campaigns, discover and refine posts, run research, find opportunities, submit work, and measure results. Gaps exist but are minor for the central purpose—product listings can’t be updated or deleted, and delegates can be added but not removed.