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WillHsiaoNYC

NYC Open Data Capital Projects MCP Server

by WillHsiaoNYC

rank_projects

Rank capital project schedules or budgets by metrics like spend percentage or budget variance. Filter by program category, agency, or delay status to prioritize actions.

Instructions

Rank schedules (entity='schedule', rows=PIDs) or budgets (entity='budget', rows=FMS lines). rank_by must be NATIVE to entity; the other domain is filter-only. Echoes ranked_entity. Budget rank_by: total_budget | spend_to_date | spend_pct | budget_variance (last-period delta) | cumulative_budget_change (latest - original budget). Optional category (see list_categories) filters to one program type, e.g. 'Library'. Optional agency scopes to one agency; agency_role ('auto'|'sponsor'|'managing') picks the lens — 'auto' uses the owner (sponsor) view, except DDC/DCAS/EDC default to builder (managing). Echoes agency_scope.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYes
rank_byYes
nNo
directionNotop
min_total_budgetNo
max_total_budgetNo
delayed_onlyNo
categoryNo
agencyNo
agency_roleNoauto
Behavior3/5

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

Given no annotations, the description carries full burden. It discloses that the tool echoes ranked_entity and agency_scope and explains agency_role default behavior. However, it does not mention safety traits (read-only, permissions) or response pagination/limitations, leaving some behavioral aspects unclear.

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?

The description is a single paragraph that efficiently conveys core functionality and key parameters. It is front-loaded with purpose and avoids filler. While a bullet list could improve scannability, every sentence contributes meaning, making it concise for the information density.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description only hints at return values ('Echoes ranked_entity' and 'agency_scope') without specifying format, fields, or pagination. For a complex tool with 10 parameters, this is a notable gap, though the behavioral details partially compensate.

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

Parameters3/5

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

With 0% schema description coverage, the description explains several parameters (entity, rank_by, category, agency, agency_role) with concrete examples and default logic. However, it omits details for n, direction, min/max_total_budget, and delayed_only, so coverage is partial but adds value beyond 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?

The description clearly states the tool ranks schedules or budgets based on entity and rank_by, with specific examples for budget rank_by values. It distinguishes between two entities (schedule and budget) and lists optional filters, making the purpose unambiguous and distinct from siblings.

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?

The description does not explicitly state when to use this tool versus alternatives like get_project_budget or schedule_breakdown. It implies usage for ranking but lacks 'when not to use' or comparative guidance, so usage context is only implicit.

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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