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Glama

Bcb Br

by io.github.pipeworx-io

Focus Expectations

focus_expectations
Read-onlyIdempotent

Focus market expectations survey — annual median/mean forecasts from ~150 economists. Filter by indicator (e.g. "IPCA", "Selic", "PIB Total", "Câmbio", "IGP-M") and optionally reference year. Returns latest survey rows with Media, Mediana, Minimo, Maximo, numeroRespondentes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topNoMax rows to return (default 10, max 100). Ordered by survey Data descending.
indicatorYesIndicador name in Portuguese, e.g. "IPCA", "Selic", "PIB Total", "Câmbio".
reference_yearNoDataReferencia year to filter, e.g. "2026". Optional.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "indicator": "IPCA",
+    "reference_year": "2026"
+  },
+  {
+    "indicator": "Selic",
+    "top": 5
+  }
+]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare read-only and idempotent behavior. The description adds valuable context about the return fields and the nature of the data (annual median/mean forecasts), which goes beyond annotations. No contradictions found.

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?

Two concise sentences convey the tool's purpose, filter options, and return fields without redundancy. The description is front-loaded with the main function and examples, making it efficient.

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?

Even without an output schema, the description lists the return columns, providing a clear expectation of the data. Combined with the schema and annotations, the information is sufficient for this tool's complexity. A small gap is the lack of mention of the 'top' parameter, but it is documented in the schema.

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?

Schema coverage is 100% and each parameter has a description. The description adds marginal extra parameter context (e.g., an additional indicator example 'IGP-M'), but does not significantly enrich beyond what the schema already provides.

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 accesses the Focus market expectations survey, with specific details on the data source (~150 economists) and return fields (Media, Mediana, etc.). It distinguishes itself from sibling tools by focusing on this specific survey and indicator filtering.

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 provides clear context for use: filter by indicator and optionally reference year. It doesn't explicitly mention alternatives or exclusions, but the context is specific enough for an agent to infer when to invoke it.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool has a distinct purpose; even similar tools like ask_pipeworx and ask_pipeworx_grounded are clearly differentiated by grounding behavior. Polymarket tools are separated by specific angles (arbitrage, edges, tracking, fill risk, cross-venue).

Naming Consistency5/5

All tool names use consistent snake_case with descriptive verbs (ask_, compare_, discover_, generate_, list_, recall_, etc.). No mixing of camelCase or other conventions.

Tool Count4/5

35 tools is on the higher end but justified by the breadth of functionality: Brazilian economics, Pipeworx data querying, company analysis, Polymarket betting, memory, subscriptions, etc. Each tool seems necessary, though a few could potentially be consolidated.

Completeness4/5

The tool set covers major CRUD operations and data retrieval across multiple domains. Minor gaps exist (e.g., no tool to edit subscriptions directly, but unsubscribe/resubscribe works). Overall, the surface is well-rounded for the stated purposes.

Resources