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A Model Context Protocol server for discovering and analyzing government funding opportunities with intelligent grant scoring, hidden opportunity detection, and strategic application planning
The server defines 4 tools with reasonable verb-based naming and detailed descriptions (150-250 chars each). However, significant gaps exist in parameter documentation, schema completeness, and error handling guidance. Tool names are action-oriented (grant_match_scorer, hidden_opportunity_finder, strategic_application_planner, agency_landscape) and descriptions explain WHAT the tools do. However, most parameter descriptions are minimal or generic, input schemas lack constraint specifications (enums, ranges), and output schemas are not documented. The server provides analytical grant-matching capabilities but lacks the rigor expected in production tool design.
Maps agencies and their funding focus areas with comprehensive analysis. Provides insights into agency funding patterns, specializations, cross-agency collaboration opportunities, and funding landscape overview
Intelligent grant scoring system with multi-dimensional analysis. Evaluates grant opportunities across five key dimensions: Technical Fit (alignment with research interests/expertise), Competition Index (using NIH/NSF methodologies), Success Probability (likelihood of winning based on multiple factors), ROI Score (return on investment considering effort and risk), Timing Score (preparation adequacy and deadline competition)
Discovers undersubscribed grants and hidden funding opportunities. Uses visibility analysis, undersubscription scoring, and cross-category analysis to identify grants with reduced competition due to visibility, timing, or specialization factors
Portfolio optimization and timeline management tool. Categorizes opportunities into reach, match, and safety buckets, optimizes application timeline to maximize success while respecting user capacity constraints, and provides strategic recommendations for grant portfolio
Input schemas lack constraint specifications (enums, ranges, patterns). Parameters like 'analysis_type', 'optimization_strategy', 'risk_tolerance' accept free-form strings with no declared valid values, inviting hallucinated inputs from LLMs.
Output schemas are not documented. Tool descriptions mention what they return (e.g., 'detailed breakdowns', 'portfolio analysis') but the actual structure, fields, types, presence of chaining IDs, is not specified. LLMs cannot plan downstream calls without knowing response structure.
Parameter descriptions are generic or missing detail. 'search_filters' is described as 'Advanced search filters (optional)' but does not specify what filters are supported. 'user_profile' and 'capacity_constraints' are described as objects but lack field documentation.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | C | 64 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 29 | - | v1 |
No error handling guidance in tool descriptions. Tool descriptions do not specify when tools fail, what errors are retryable, or how to recover. For example, grant_match_scorer does not document failures when opportunity_ids are invalid or API rate limits are hit.
Pagination not explicitly documented. hidden_opportunity_finder accepts 'max_results' but does not specify default behavior, whether results are ordered, or how to fetch subsequent pages. LLMs may make multiple calls unaware of pagination support.
Object parameters lack nested field documentation. 'search_filters' and 'scoring_weights' are typed as objects but do not document which fields are required, their types, or valid values. This forces LLMs to guess at valid structures.