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Glama

VarynForge

by com.varynforge

Get research status

get_research_status
Read-only

Poll the latest research run for a project — runId, status, current pipeline phase, progress percent, elapsed seconds, and structured failure diagnostics when failed. failure carries failureClass (upstream_timeout | upstream_rate_limited | invalid_input | internal | unknown), the phase the run died in, retryable (true = transient upstream issue, safe to retry; false = retrying will fail again — fix inputs or report it; null = unknown, retry at most once before reporting), and the raw message. A running run with phase "initializing" and large elapsedSeconds is stalled, not queued — report it instead of waiting. Token-light: returns a small fixed shape, no step payloads.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextYesOne sentence: what is the operator trying to achieve right now? Describe their goal, not this tool's purpose.
projectIdYes

Schema Changelog

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

  1. Changed2 schema fields changed
    • addedInput schema / properties / context
      Added value: +{
+  "description": "One sentence: what is the operator trying to achieve right now? Describe their goal, not this tool's purpose.",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "projectId"
-]New value: +[
+  "projectId",
+  "context"
+]
  2. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations only declare readOnlyHint=true, but the description adds substantial behavioral detail: the failure taxonomy (failureClass values), retryable semantics, the stalled-initializing heuristic, and the token-light response shape. This goes far beyond the structured annotation and tells the agent what to expect and how to act on edge cases.

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?

Every sentence carries operational value: the return shape, failure diagnostics, retry guidance, stalled-run detection, and token budget. The description is dense but well-organized, front-loading the core purpose and return fields before edge-case guidance.

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?

With no output schema, the description carries the full burden of explaining the return value, and it does so thoroughly, including failure structure and follow-up actions. It is complete for a read-only polling tool; the only minor gap is parameter semantics, which is already partially addressed by the schema.

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?

The input schema describes context well but projectId only through format and pattern, and schema description coverage is only 50%. The tool description merely says 'for a project' and does not explain the required context parameter or the semantic meaning of projectId, so it does not compensate for the schema gap.

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 opens with a specific verb and resource: 'Poll the latest research run for a project' and then enumerates the exact returned fields (runId, status, pipeline phase, progress, elapsed seconds, failure diagnostics). This clearly differentiates it from sibling status tools like get_account_status or get_draft_status by naming the resource and scope.

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 establishes a clear polling context and provides conditional guidance: stalled initializing runs should be reported rather than waited on, and retryable failures are safe to retry while non-retryable ones are not. It does not explicitly name alternative tools or state when not to use it, but the polling intent and decision rules are clear.

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

A3.8/5.0
Disambiguation4/5

Most tools target a distinct resource and action, and the descriptions carefully cross-reference close alternatives (e.g., add_article_suggestion vs create_article_suggestion_with_input vs accept_idea). A few brief/read variants like get_article_brief, get_write_handoff, and download_brief_markdown could still be confused despite helpful explanations, so the set is not perfectly unambiguous.

Naming Consistency5/5

Tool names follow a highly consistent verb_noun snake_case pattern throughout: get_*, list_*, create_*, update_*, set_*, add_*, delete_*, start_*, expand_*, etc. Even the less common names like lint_draft and remap_asset are still clear verb_noun constructions.

Tool Count1/5

At 57 tools, this far exceeds the 50+ extreme threshold for a single MCP server. Even for a broad content workflow, the surface is overwhelming and would benefit from consolidation or splitting into focused servers.

Completeness4/5

The tool set covers the full content lifecycle: project setup, research runs, opportunity clustering, suggestion creation, brief generation, drafting, linting, publishing, reporting, and account management. Minor gaps exist—destinations and projects cannot be deleted via MCP, and there is no direct update for article suggestion metadata—but these are workable via the web UI or existing tools.

Resources