Skip to main content
Glama
0103juan

secop-mcp

by 0103juan
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

query_contracts

Filter and aggregate SECOP II contracts by supplier, entity, date, or value to get totals and counts; returns up to 100 rows.

Instructions

Query SECOP II contracts. Returns at most 100 rows.

With aggregates and columns together, rows are grouped by the columns (for example
columns=[proveedor_adjudicado, documento_proveedor] with sum of valor_del_contrato gives the
total per supplier). Filters are combined with AND; 'contains' is a case-insensitive
substring match on a text column. Dates are AAAA-MM-DD. order_by takes a requested column
or an aggregate alias such as sum_valor_del_contrato or count.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
columnsNo
filtersNo
order_byNo
aggregatesNo
descendingNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior4/5

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

With no annotations provided, the description carries the full behavioral burden and does so well: it discloses the 100-row cap, that aggregates+columns cause grouping by those columns, that filters combine with AND, that 'contains' is a case-insensitive substring match, the date format, and how order_by resolves to aggregate aliases. It omits pagination/offset behavior and auth expectations, but the core semantics are unusually well surfaced.

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 purpose and row cap before semantics, and each sentence earns its place by documenting a distinct behavior. The prose is dense but not padded; only the trailing order_by example overlaps slightly with the aggregate/grouping point.

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 six-parameter tool with no output schema and no annotations, the description covers result shape (≤100 rows), filter semantics, grouping, dates, and ordering adequately. Remaining gaps are pagination behavior and default column selection, which are minor relative to what is provided.

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 description coverage is 0%, so the description must compensate, and it does for most parameters: filters (AND combination, 'contains' semantics), dates (AAAA-MM-DD), order_by (column or aggregate alias like sum_valor_del_contrato/count), aggregates+columns grouping, and limit via the 100-row cap. 'descending' is never explained, which is the one meaningful gap.

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 opens with a specific verb+resource ('Query SECOP II contracts') and immediately bounds the result ('at most 100 rows'), so the agent knows exactly what operation this is. It does not distinguish itself from the siblings describe_dataset and find_entity, leaving the agent to infer the boundary between querying and those two tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use or when-not guidance, and no mention of the sibling tools (find_entity, describe_dataset) that an agent would need to choose between. Usage is only implied by the description of query mechanics, so the agent gets no routing help.

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

Deploy Server

Other Tools