Skip to main content
Glama

Search lenders, BDCs, borrowers, sponsors and credit funds

search_private_credit
Read-onlyIdempotent

The capital structure graph behind private markets, built from every BDC's schedule of investments each quarter since 2022. query is a name (trigram matched across names and aliases: Ares, Ares Capital and ARCC resolve to the adviser, the BDC and its ticker as distinct rows); without a query, filters list providers (class, state, BDC advisers only) or borrowers (sponsor, industry, minimum BDC lenders, maturity before a date, sort). Every row names its fact class (filed: the BDC's own figure; derived: arithmetic over filed figures, a sum of pieces is a lower bound; carried: from another DFX graph with its source; inferred: an attribution by rule with confidence). A mark below cost is a mark, not impairment; a moved maturity is an observed term change, not an amendment; non-accrual and default are not on the tape and are never inferred.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNo
classNoProvider class (probable or confirmed).
limitNo
queryNo
stateNoTwo-letter US state code.
cursorNonext_cursor from a previous page of this tool, unchanged.
industryNoIndustry as a filer wrote it (contains).
held_onlyNo
entity_typeNoDefault: any type with a query; providers without one. borrower lists exclude grade C groups.
min_lendersNoBorrowers held by at least this many BDCs.
sponsor_dfx_idNoA private credit graph id of the form dfx:pc:<uuid> (from search_private_credit, resolve_name or search_entities).
maturity_beforeNoISO date: borrowers whose next dated maturity is on or before it.
bdc_advisers_onlyNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.3/5.0
Behavior5/5

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

Annotations already establish the safe-read, idempotent, closed-world profile, so the description's job is to add interpretive context—and it excels. It discloses the fact-class taxonomy (filed vs derived vs carried vs inferred) with the lower-bound caveat for summed pieces, and it pre-empts misreading with explicit guardrails: a mark below cost is not an impairment, a moved maturity is not an amendment, and non-accrual/default are never present. This directly prevents an agent from drawing false conclusions from result rows.

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?

Three dense sentences, each earning its place: scope and provenance first, then query-vs-filter mechanics, then fact-class semantics and interpretive guardrails. The density is justified given 13 parameters, no output schema, and high misinterpretation risk; nothing is fluff and the most critical caveats are saved for the end where they reinforce rather than distract.

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 this complex—13 params, no output schema, ambiguous query resolution—the description covers the data provenance, mode selection, fact classes, and interpretive guardrails well. What is missing is the result shape (row fields, pagination/next_cursor behavior beyond the schema's one-liner) and the semantics of `sort` and `held_only`. Since there is no output schema, the description carries more of the return-value burden than it fully discharges.

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 62%, so the schema handles roughly half the parameters. The description compensates where it matters most: `query` has no schema description but gets rich semantics here (trigram matching across names/aliases, distinct rows for adviser/BDC/ticker). It also explains how the filter parameters divide into provider-listing vs borrower-listing modes. Gaps remain for `sort`, `held_only`, and `limit`, which are neither described in the schema nor the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the resource (the private-market capital structure graph) and the data source (every BDC's schedule of investments since 2022), and states the tool searches lenders, BDCs, borrowers, sponsors, and credit funds. It distinguishes itself from siblings through its provenance and its query-vs-filter mechanics, though it never names a sibling it is not, so differentiation is implicit rather than explicit.

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 gives clear within-tool usage context: `query` is for name resolution (with trigram/alias semantics), and without a query, filters list either providers (class, state, BDC advisers only) or borrowers (sponsor, industry, minimum BDC lenders, maturity before, sort). It does not, however, name alternative sibling tools or state when to prefer them over this one, so cross-tool routing is left to the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.