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Glama

get_contributor_score

A contributor's CLV record.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contributor_idYes

Schema Changelog

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

  1. First observed

TDQS

C2.1/5.0
Behavior1/5

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

No annotations are provided, so the description should carry behavioral meaning, but it only defines the object. It does not state whether this operation is read-only, what the API returns, what CLV represents, or any rate/scope information. The agent has almost no insight into the tool's actual behavior.

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

Conciseness2/5

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

The text is extremely short, but this is under-specification rather than conciseness. It does not provide enough actionable information for an agent to rely on, so the brevity is not effective.

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

Completeness2/5

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

While this is a simple tool with a single parameter and no output schema, the description omits key context like the meaning of CLV, the return shape, and any operational considerations. Overall, the documentation is minimal and offers little more than the tool name alone.

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 description coverage is 0%, so the description must compensate. The phrase 'contributor's' implicitly maps to the contributor_id parameter, but it adds no extra semantics such as ID format, constraints, or examples. The schema only stores type string, so the tool remains essentially undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'A contributor's CLV record' identifies a resource but lacks a clear verb. It is not a pure tautology, but CLV is left undefined and the tool's behavior (retrieving/creating/updating/) is ambiguous. It does not clearly distinguish this from sibling tools like get_value_bets or get_track_record.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus any alternative. No context is given for which scenarios require fetching a contributor's CLV record, and no sibling tools are mentioned or excluded.

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

B3.2/5.0
Disambiguation4/5

Most tools target clearly distinct resources (predictions, bets, balance, leaderboard, record). However, get_predictions and get_value_bets are quite similar in scope—one being the unfiltered version of the other—so an agent could misselect without clearly reading paywalled requirements.

Naming Consistency4/5

The get_ prefix is used for most retrieval operations, with place_bet, submit_prediction, fetch, and search as notable exceptions. The verb-noun structure is otherwise consistent, so the deviations are minor.

Tool Count5/5

The 13-tool surface is proportional to the server's purpose of enabling paper betting, prediction retrieval, and performance tracking. Each tool maps to a distinct action within those workflows without feeling redundant.

Completeness5/5

The domain covers the full lifecycle: searching/fetching predictions, submitting and valuing predictions, placing and viewing bets, checking balance, and viewing performance via track record, CLV scores, and leaderboard. No obvious gaps prevent core workflows.