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Glama

Get a run bundle

get_run

Returns the canonical result bundle of one run (schema result.v1): protocol lock hash, provenance, reproduce command, per-participant outcomes with intervals. A PRE-REGISTERED run, whose protocol is frozen and timestamped but which has not been executed, returns state PRE_REGISTERED with measured false, its lock and its frozen files, and no result: nothing has been measured yet.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
run_idYesRun id, e.g. 'TRANSCRIPTION-001' (see list_categories, field run_id)

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the special PRE_REGISTERED state behavior: returns state PRE_REGISTERED, measured false, lock and frozen files, and no result because nothing has been measured. This goes well beyond a simple 'get' description, though it does not mention errors, permissions, or pagination.

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 dense but efficient: the main content is stated first, followed by a precise edge-case explanation. Every sentence adds value, and the special PRE_REGISTERED behavior is clearly separated without unnecessary prose.

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?

For a simple one-parameter tool with no output schema and no annotations, the description is remarkably complete. It names the return schema, lists expected result components, and covers the pre-registered edge case. An agent has enough information to call the tool correctly and interpret its response.

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?

The schema already provides 100% coverage of the single parameter run_id, including an example format. The description adds the context that it identifies 'one run' and connects to list_categories, but does not add substantial new meaning 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 states a specific verb ('Returns'), a clear resource ('canonical result bundle of one run'), and enumerates the contents (protocol lock hash, provenance, reproduce command, per-participant outcomes). It also names the schema 'result.v1', making it easy to distinguish from sibling tools like get_results.

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 clearly implies when to use the tool: to fetch the canonical result bundle for a single run, identified by run_id. It does not explicitly name alternatives or exclusion conditions, but the 'one run' scope and reference to list_categories for run_id provide sufficient context.

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

A4.1/5.0
Disambiguation5/5

Each tool has a clearly distinct job: list categories, get category-level results, get run-level bundles, and explain interpretive limits. The overlap in concepts like 'limits' and 'answer rule' is acceptable because explain_limits is explicitly about interpretation while get_results is about the evidence-based answer.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with simple, specific actions: list, get, get, explain. The naming makes both the action and the resource predictable across the set.

Tool Count5/5

Four tools is a well-scoped size for a read-only evidential querying service. Each tool earns its place and there is no filler or unnecessary duplication.

Completeness4/5

The read/interpretation flow is covered: list categories, fetch category results, fetch run result bundles, and explain limits. The only minor gap is the lack of a direct run-listing endpoint, though list_categories provides source-run pointers and get_run can fetch specific runs.

Resources