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find_methodology

Find methodology approaches for a specific research task. Returns structured method-level results (not raw chunks): method name, key idea, dataset used, performance metric. Filters by task domain, dataset, metric. Built on LLM-classified contentType=methodology chunks combined with benchmark results JOIN. Use this instead of search when you want HOW researchers approach a problem rather than 10 papers about it. Note: surfaces any chunk classified as methodology, including ones where the task is mentioned only as a toy example. Filter by category (e.g. cs.CV for image tasks) to narrow scope. This searches EXISTING papers for methods others have published (literature search) — it is NOT a guide for conducting your own research: for a step-by-step scientific method tailored to your own research question, start with the methodist door.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesResearch task: "relation extraction", "question answering", "image classification"
limitNoMax results to return
dateToNoFilter: published on or before (ISO date)
detailNo'standard'/'full' invoke an extra LLM extraction step to surface method_name + key_idea (~1.5s overhead). 'minimal' skips it.
metricNoEvaluation metric: "F1", "accuracy", "BLEU"
run_idNoOptional. The active methodist run_id (as returned by the methodist diagnose / get_current_dose door). Pass it whenever you call this tool while working inside a run, so the call is attributed to that run for the §8 usage crosscheck — attribution is run-anchored, so it stays correct even if your access token refreshes mid-run. Must be YOUR run: a run_id owned by a different principal, or a non-existent run_id, is rejected.
datasetNoSpecific dataset name: "SQuAD", "ImageNet", "GLUE"
dateFromNoFilter: published on or after (ISO date)
frameworkNoML framework filter: "PyTorch", "TensorFlow"
categoriesNoFilter by arXiv categories (e.g. cs.AI, cs.LG)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • removedInput schema / properties / detail / default
      Removed value: -"full"
  2. Changed1 schema field changed
    • changedInput schema / properties / detail / default
      Previous value: -"standard"New value: +"full"
  3. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Annotations are absent, so the description carries full burden. It discloses non-obvious behavior: results are structured method-level aggregates built from LLM-classified methodology chunks joined with benchmark results, and it honestly warns that toy-example mentions can surface. This helps an agent anticipate precision issues before invoking the tool.

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 front-loaded with the core purpose and each sentence carries useful information about output, data source, caveats, or sibling routing. It is slightly verbose due to restating filter concepts and a long final exclusion, but it remains efficient and well-organized.

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 tool with 10 parameters and no output schema, the description covers return fields, data provenance, a precision caveat, and the relationships to key siblings. Detailed parameter behavior is left to the schema, which is acceptable, but a concrete return example or pagination note would make it fully complete.

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?

Schema description coverage is 100% and each parameter already has rich detail (examples, defaults, enum semantics including the ~1.5s overhead for detail). The description only summarizes filters at a high level and does not add meaning beyond the schema, so the baseline 3 is appropriate.

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?

States a specific verb and resource ('Find methodology approaches for a specific research task') and describes the structured output: method name, key idea, dataset, performance metric. It additionally distinguishes itself from generic `search` and from the `methodist` door, so an agent can place it precisely among 35 siblings.

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

Usage Guidelines5/5

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

Explicitly provides selection criteria: 'Use this instead of `search` when you want HOW researchers approach a problem rather than 10 papers about it.' It also names the exclusion case, directing users to `methodist` for step-by-step research guidance, and advises category filtering to narrow scope.

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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