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Glama
mayai-it

bandiradar-mcp

by mayai-it
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
OPENAI_API_KEYNoOpenAI API key. Required if BANDIRADAR_LLM_PROVIDER is 'openai'.
ANTHROPIC_API_KEYNoAnthropic API key. Required if BANDIRADAR_LLM_PROVIDER is 'anthropic'.
BANDIRADAR_LLM_PROVIDERNoLLM provider for relevance scoring. Options: 'anthropic' or 'openai'. If not set, falls back to offline heuristic.

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
list_sourcesA

List the registered funding sources as [{id, kind}].

fetch_opportunitiesC

Ingest a source into the store, saving progressively.

Returns a structured SourceResult dict: source, status
(ok/partial/failed/empty), fetched / mapped / new / amended / skipped_invalid,
error, duration_s, started_at / finished_at. ``sample`` defaults to True so it
is usable offline with zero secrets.
search_opportunitiesA

Rank opportunities for a profile (offline in sample mode).

Accepts EITHER ``profile_path`` OR an inline ``profile`` dict. Returns ranked
canonical views — no raw payloads. ``mode`` is the operating point
(precision|balanced|recall; precision needs an LLM key); an explicit ``min_score``
overrides it. ``with_benchmarks`` adds ANAC historical benchmark notes;
``with_documents`` folds attachment-PDF text into matching.
score_opportunityC

Score one stored opportunity for a profile; returns a single Match dict.

get_matchesA

Return PERSISTED matches for this profile_version (no recompute).

get_profileC

Return the parsed profile as a dict.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.3/5.0

Scored across 6 tools

Disambiguation5/5

Each tool has a distinct role in the workflow: listing sources, ingesting opportunities, ranking opportunities, scoring a single opportunity, retrieving persisted matches, and inspecting the profile. Even the related matching tools are clearly separated by computation versus persistence.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern with clear, imperative verbs. There is no mixing of conventions or vague generic names.

Tool Count5/5

Six tools is a well-scoped size for a funding opportunity matching server. Each tool covers a meaningful part of the workflow without redundancy or bloat.

Completeness4/5

The core lifecycle of fetching sources, searching/ranking opportunities, scoring individual opportunities, and retrieving persisted matches is covered. Minor gaps exist around direct opportunity detail retrieval and profile management, but these are workable through existing tools.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources