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Glama

Carbon Interface

Estimate Flight

estimate_flight
Read-onlyIdempotent

Estimate CO2 emissions from a flight. Provide number of passengers and flight legs (departure/arrival airport IATA codes). Returns per-passenger and total carbon emissions. Example: estimate_flight(2, [{"departure_airport": "SFO", "destination_airport": "JFK"}]).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
legsYesArray of flight legs with IATA airport codes
_apiKeyYesCarbon Interface API key
passengersYesNumber of passengers (e.g., 2)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
legsYesFlight legs with airport codes
carbon_gYesCarbon emissions in grams
carbon_kgYesCarbon emissions in kilograms
carbon_lbYesCarbon emissions in pounds
carbon_mtYesCarbon emissions in metric tons
passengersYesNumber of passengers
estimated_atYesISO timestamp of estimate

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds that it 'returns per-passenger and total carbon emissions,' disclosing the output format without contradicting the annotations. No mention of rate limits or API key handling, but the annotations lower the burden.

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?

Three sentences and a compact example deliver purpose, inputs, outputs, and a usage pattern with zero wasted words. The first sentence is front-loaded with the core purpose, making it easy to scan.

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?

With rich annotations and a fully covered schema, the description covers purpose, inputs, outputs, and provides a worked example. It does not discuss edge cases or the required _apiKey, but the schema already marks it required. Completeness is strong for a simple estimation tool.

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 coverage is 100%, so the baseline is 3. The description adds value by clarifying that 'passengers' and 'legs' are provided structurally, and the example illustrates the nested leg object with IATA codes. This natural language explanation complements the schema descriptions.

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 opens with 'Estimate CO2 emissions from a flight,' a specific verb-resource pair that clearly distinguishes this from sibling tools like estimate_electricity and estimate_vehicle. The scope (flight) and output (CO2 emissions) are unambiguous.

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 states what inputs to provide ('number of passengers and flight legs...') and includes a concrete example, making the invocation context clear. It does not explicitly name alternative tools for non-flight emissions, but the resource scope ('from a flight') implies the appropriate use case.

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

A3.8/5.0
Disambiguation2/5

Multiple tools are near-duplicates or heavily overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical, several polymarket_* tools cover the same edge-detection domain, and scan_competitor_ai_presence is just a wrapper around ai_visibility_check. Agents will frequently struggle to pick the right tool.

Naming Consistency3/5

Most tools use snake_case, but the pattern is inconsistent: verb_noun (estimate_electricity, resolve_entity), noun_phrase (entity_profile, recent_changes), brand-specific (ask_pipeworx, pipeworx_trending), and family-prefixed (polymarket_*). The naming is readable but lacks a single cohesive convention.

Tool Count2/5

At 34 tools, the set is far larger than the 'Carbon Interface' name implies. It piles together carbon estimation, a massive data-router, prediction-market analysis, memory, subscriptions, and meta-tools, making the surface feel bloated and unfocused.

Completeness3/5

The carbon-estimation purpose is thin (only three estimators with no lifecycle), but the broader Pipeworx/data and prediction-market subdomains are fairly well covered. Gaps exist in each subdomain (e.g., no order placement for betting, no carbon scope beyond the three estimates), and the lack of a clear primary domain makes coverage hard to assess.