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Glama

x402_demand_report

What are agents asking for that they cannot buy yet, aggregated over 30 days. From our own measurements: calls we rejected on an uncovered value where the caller had already presented payment (kind is typically coin, country, vies_country, monitor_host or sms_service — free-form values possible), free requests filed by agents, and settled $0.01 intents. Demand at one provider, not a network-wide picture. Aggregates only: no wallets, no free text, no subject ids.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceNoWhether this answer came from the 6-hour cache or was aggregated on the spot. Same content either way.
coverageYesWhat this report was built from and how far back it can see.
checked_atYesWhen we produced this answer (ISO 8601, UTC). The only ISO timestamp in the payload; every other timestamp is unix seconds.
disclaimerYesVerbatim boundary: demand at one provider, shaped by what we happen to sell — absence of demand here is not evidence of absence of a market.
paid_wantsYesPaid $0.01 intents in the window whose payment actually settled and which came from external traffic, as counts only. Deliberately without kind/value rows, wallet hashes or intent text.
methodologyYesExactly how each number is counted, including which traffic is excluded and what is never included.
generated_atNoWhen the aggregate itself was computed; it is cached for up to 6 hours, so this can be older than checked_at.
unmet_demandYesCalls we had to reject because a requested capability value is not covered, counting ONLY rejections where the caller had already presented a verified payment proof — the strongest signal here, because somebody was demonstrably willing to pay. Rejections that happen before the payment proof are free to produce and are excluded on purpose. External traffic only, top 50 by calls.
filed_requestsYesCoverage gaps agents filed themselves via the free request_capability tool, per (kind, value), top 50 by count. Free-text notes are never included. Free intake is capped per sender per day through an eventually consistent store, so that cap is approximate.

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, description fully discloses behavior: 30-day aggregation, data sources, and exclusions (no wallets, free text, subject ids). It clearly defines scope and limitations.

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?

Four concise sentences, each adding value. No unnecessary information; well-structured and front-loaded.

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

Completeness5/5

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

For a tool with no parameters and an output schema, the description covers purpose, data sources, aggregation period, and scope limitations comprehensively.

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?

Tool has zero parameters, so baseline is 4. No additional parameter semantics needed.

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?

Description clearly states the tool aggregates demand data (what agents ask for but cannot buy) over 30 days, from specific sources (rejected calls, free requests, settled intents). It distinguishes itself from sibling tools like x402_network_report by noting 'Demand at one provider, not a network-wide picture.'

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?

Description indicates when to use: for provider-specific demand data, not network-wide. It implicitly suggests alternatives (network report) but does not explicitly state when not to use.

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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Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.1/5.0
Disambiguation5/5

Every tool targets a distinct action and domain: country-specific company checks (check_ch_company, check_fr_company, etc.), insolvency checks, crypto signals, x402 network tools, etc. Descriptions are detailed and make boundaries clear, with no two tools appearing to do the same thing.

Naming Consistency5/5

Tool names follow very consistent patterns: 'check_<country>_<entity>' for registers, 'crypto_*' for crypto intelligence, 'x402_*' for x402 network functions, 'screen_*' for screening, and a few free-form names like 'verify_eu_vat' and 'lookup_lei' that still fit the verb_noun style. No mixing of conventions.

Tool Count4/5

39 tools is high but justified by the broad scope: the server aggregates many country-specific checks, insolvency registers, crypto tools, x402 monitoring, and auxiliary functions (like phone number buying, LEI lookup). Each tool serves a clear purpose, and the count is not excessive given the coverage. A slight reduction could be possible by merging some country checks, but overall it's reasonable.

Completeness4/5

The server covers major European company registers (CH, UK, FR, NL, NO, PL, CZ, FI), insolvency checks (NL, FR), sanctions screening, VAT validation, crypto market intelligence, and x402 network tools. It acknowledges gaps (e.g., no German check) and provides fallback tools like 'screen_eu_supplier' and 'file_agent_want'. Minor missing pieces (e.g., Italian company check) keep it from a perfect 5.

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