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find_io_fanout

Rank entry-point methods by total I/O calls with serial and parallel breakdown, optionally estimating latency floor from ingested performance data.

Instructions

Return entry-point methods ranked by I/O call count, with serial/parallel breakdown.

For each HTTP/Kafka/Scheduled entry point, reports the total number of I/O
operations (DB + HTTP + cache) detectable in its method body, split into
serial (latency adds) and parallel (latency = max of group).

If perf data has been ingested via ingest_perf_results, also estimates
latency_floor_ms = sum(serial p99s) + max(parallel p99s).

Args:
    repo_name: Repository to query.
    min_total: Only return methods with at least this many I/O calls (default 2).

Returns:
    List of dicts: endpoint_path, http_method, handler_fqn, file_path,
    line_start, total_io, serial_io, parallel_io, parallel_wrapper, latency_floor_ms.
    Sorted by total_io descending.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repo_nameYes
min_totalNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, description explains the breakdown, latency estimation condition, and result sorting. However, it omits potential limitations (e.g., static analysis only, dependency on indexing), and does not state read-only nature.

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?

Description is well-structured with paragraphs for purpose, detail, parameters, and output. Slightly verbose but front-loaded with the core purpose. Could be shortened by merging parameter explanations.

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?

Given the tool's complexity and lack of output schema, description provides return fields, sorting order, and prerequisite for latency calculation. Omits if results are real-time or cached, but overall comprehensive for a query tool.

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

Parameters4/5

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

Schema coverage is 0%, but description adds meaning for both parameters. It explains repo_name as repository to query and min_total with default and purpose, complementing the sparse 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?

Description clearly states 'Return entry-point methods ranked by I/O call count, with serial/parallel breakdown.' It identifies the specific resource and metric, setting it apart from siblings like find_entry_points.

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

Usage Guidelines3/5

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

Description mentions when latency estimation is available (if perf data ingested) but lacks explicit when-not-to-use or comparisons to sibling tools like find_complexity_hints. No guidance on when not to use.

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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