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lduda79

research-mcp

by lduda79

analyze_project

Analyze all runs of a project: aggregate metrics, hyperparameters, correlations, and detect outliers to inform next experiments.

Instructions

Fasst ALLE Laeufe eines Projekts in einem Aufruf zusammen - fuer die Gesamtanalyse.

Das ist das richtige Tool fuer Fragen wie "analysiere alle meine Testlaeufe",
"welche Hyperparameter haengen mit dem Ergebnis zusammen", "gibt es Ausreisser"
oder "was sollte ich als naechstes testen". Liefert in einem Objekt:

- jeden Lauf mit flachen Hyperparametern und zusammengefassten Metriken
- welche Hyperparameter ueberhaupt variiert wurden und welche konstant sind
- Korrelationen zwischen numerischen Hyperparametern und JEDER Metrik
- die verfuegbaren Metriknamen und die Laeufe mit hoher Fold-Streuung

Die Korrelationen sind deskriptiv und beruhen oft auf wenigen Laeufen - sie
sind Anhaltspunkte, kein Kausalnachweis. Deute sie im Kontext.

Fuer Vorschlaege, was als Naechstes zu testen ist, kannst du die Befunde
anschliessend mit search_papers gegen die Literatur abgleichen.

Args:
    projekt: Name des Projekts, z.B. "masterarbeit"
    metric: Optional die Zielmetrik, die im Fokus stehen soll, z.B.
            "std_val_dbm_mse". Wird sie weggelassen, waehlt das Tool selbst
            eine aus - korreliert wird ohnehin gegen alle Metriken. Die
            gueltigen Namen stehen im Feld "verfuegbare_metriken".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
metricNo
projektYes
Behavior5/5

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

With no annotations, the description fully discloses behavior: returns per-run data, varied/constant hyperparams, correlations, metric names, and high-variance runs. Warns correlations are descriptive, not causal. Also mentions auto-selection of metric if omitted.

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?

Well-structured with clear sections, but slightly verbose. Could be more concise while retaining all essential information. Every sentence contributes value, so no major waste.

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?

Given no output schema and no annotations, the description covers all necessary aspects: purpose, usage, parameter details, return content, and interpretation caveats. Completely adequate for an analysis tool.

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

Parameters5/5

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

Schema has no descriptions (0% coverage). Description explains 'projekt' with an example and 'metric' as optional with auto-selection behavior and guidance to find valid names in output field 'verfuegbare_metriken'. Adds significant meaning.

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 clearly states the tool aggregates all runs of a project for overall analysis, with specific example questions. It distinguishes itself from siblings like list_experiments and get_experiment by focusing on cross-run analysis.

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?

Provides explicit when-to-use guidance with example queries and suggests using search_papers for literature checks. Does not explicitly state when not to use or list alternatives, but context is 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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