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ol_bdc_top_borrowers

Read-only

MOAT / BDC discovery: the private-credit borrowers syndicated across the MOST BDCs, ranked by ACTIVE lender count (holder_count_active), then holder_count, then exposure -- the entrypoint for the BDC/private-credit category. Returns {summary, count, borrowers}; each row is {borrower, borrower_norm (the key other ol_bdc_* tools take), holder_count, total_fair_value (whole USD, latest filings), industry}. Feed a borrower_norm into ol_bdc_borrower_dispersion for cross-lender pricing. Caps: limit default 25 / hard 100; min_holders default 2 (max 50). Parser mis-ingests are filtered out when the filter is available (borrower_filters says whether it ran). Source: SEC EDGAR BDC schedules of investments (Oxford Ledge parse); FREE. Caveats ride the response's tool_notes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax borrowers to return (default 25, hard cap 100).
min_holdersNoMinimum number of BDC lenders a borrower must appear in (default 2).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, it discloses the ranking tiebreak order, the limit/min_holders caps, that parser mis-ingests are filtered when the filter is available and that `borrower_filters` reports whether it ran, and that caveats ride in tool_notes. That is materially useful behavioral context for a no-annotation-rich 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?

Front-loaded with the purpose and ranking logic, then output shape, then caps and provenance – good ordering. It is dense with domain jargon and parentheticals, but nearly every clause carries operational information rather than filler.

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?

No output schema exists, yet the description specifies the response envelope {summary, count, borrowers} and the row fields {borrower, borrower_norm, holder_count, total_fair_value, industry}, plus data source, freshness, and cost. An agent has everything needed to call and interpret it.

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 100%, so the baseline is 3, but the description adds meaning: what min_holders actually gates (minimum BDC lenders a borrower must appear in) and the distinction between holder_count_active and holder_count used for ranking. That meaningfully reinforces the schema's bounds.

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 precise verb+resource: private-credit borrowers syndicated across the most BDCs, ranked by holder_count_active then holder_count then exposure. It also positions itself as 'the entrypoint for the BDC/private-credit category', distinguishing it from siblings like ol_bdc_common_borrowers and search_bdc_borrower.

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?

Explicitly routes the agent forward: 'Feed a borrower_norm into ol_bdc_borrower_dispersion for cross-lender pricing', and names itself as the category entrypoint. It does not say when NOT to use it versus the other ol_bdc_* siblings (e.g., ol_bdc_common_borrowers), so it stops short of full when/when-not guidance.

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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