mcp.xynaptic

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

No annotations are provided, so the description carries the full disclosure burden. It usefully reveals the data provenance and that only active businesses are returned, and it discloses the paywall model ($0.020 per call via x402/USDC) — genuinely valuable for an agent. However, it omits return shape, result limits, pagination, and any auth/error behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core verb and resource, then qualifiers, then the trade vocabulary, with the price appended in brackets. Dense but every clause earns its place; only the enumeration is long, and it is necessary given the absent enum in the schema.

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

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

There is no output schema and no annotations, so the description should say more about what comes back (fields, count, pagination). It covers the domain, filters, and cost adequately but leaves the return contract and any call prerequisites unspecified for a nested-object parameter tool.

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

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

Schema coverage is 100% but the schema descriptions are only terse examples ('69', 'plombier') with no enums. The description supplies the full allowed trade vocabulary (plombier, electricien, menuisier, etc.), which is meaning the schema does not provide, and implicitly clarifies that dept is a French department code.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('find French tradespeople by trade and department') plus the data source ('official registry, active businesses only'). This clearly separates it from siblings like company-search, tender-search, and property-price without needing to open the schema.

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

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

No explicit when-to-use or when-not-to-use guidance, and no routing to alternatives such as company-search or tender-search. The 'official registry, active businesses only' note describes data quality rather than selecting conditions between tools.

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

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

With no annotations, the description carries the full burden and does well: it discloses the hybrid statistical+LLM approach, that nothing is stored, the 200-row cap, and the x402/USDC payment model at $0.020 per call. It does not cover failure modes or rate limits, but the privacy and billing behavior 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Information-dense and front-loaded — what it does, what you give it, what it returns, then body shapes and pricing. It is a single long line rather than clean sentences, but every clause carries payload with no filler.

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

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

For a 2-param nested tool with no output schema, the description supplies the return fields and input constraints an agent needs to call it correctly. No annotation or output schema to lean on, and only edge-case/error behavior is left unstated.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds real semantics the schema lacks: the mutually exclusive body shapes ({transactions:[...]} vs {csv: raw rows}), the required fields per row (date, label, amount), and the max-200 constraint.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource (categorize bank transactions) and enumerates the outputs produced: category, merchant, income/expense, recurrence detection. The scoping phrase 'bring your own transactions' plus the accepted input shapes cleanly separates it from siblings like bank-statement-parse.

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

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

The description makes the invocation context obvious (you supply transactions as an object array or raw CSV), but it never says when to prefer this over bank-statement-parse, bank-reconciliation, or bank-spending-goals, nor any when-not conditions. Usage is implied rather than routed.

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

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

With no annotations, the description carries the full disclosure burden and does partially — it reveals the payment model ($0.020 per call, x402/USDC) and that data is 'live futures data'. It does not mention rate limits, latency, or response shape, leaving some gaps, but the pricing/auth disclosure is genuinely valuable context an agent must have before calling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Dense and front-loaded: identity, asset coverage, invocation example, data freshness, and price all in a few tight fragments. No filler, though the bracketed pricing note at the end is slightly less prominent than a cost signal arguably deserves.

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

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

With no annotations and no output schema, the description must cover more, and it leaves the nested 'params' wrapper and the odd presence of 'price'/'change_pct' (which read as output fields, not query inputs) unclarified. For an agent that must construct the call correctly, this ambiguity is a real gap.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds the plural 'assets=copper,gold' comma-separated syntax that is not present in the schema (which only defines singular 'asset'). It also confirms the query-parameter style, adding marginal value beyond the structured fields.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource ('live commodity prices') with an explicit asset enumeration (Brent, WTI, natural gas, copper, gold, coal, wheat) plus '20+ more' and 'daily change', which distinguishes it from sibling price tools like crypto-price and energy-price. An agent can identify the scope without opening the schema.

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

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

It shows how to invoke it ('GET ?asset=copper (or ?assets=copper,gold)'), which is useful call syntax, but gives no when-to-use guidance relative to siblings such as energy-price (which also covers natural gas) or industrial-price. Usage context is implied rather than stated.

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

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

With no annotations, the description carries the full burden, but it does add genuinely useful behavior: the data provenance (French state registry) and the paid access model (price per call, x402/USDC). It does not state read-only nature explicitly, rate limits, or error/not-found behavior, so gaps remain.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single dense sentence front-loads the tool's identity and data source, followed by the parameter and price. No filler, though the field enumeration is slightly list-heavy.

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

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

With no output schema, the description compensates by listing the returned identity fields, which is what an agent needs to judge fit. The main missing element is sibling routing/comparison and any note on whether multiple query params (name, naf, status) can be combined or are ignored.

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

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

Schema description coverage is 100%, so the nested params (naf, name, siren, status) are already documented with examples. The description highlights siren as the primary key, which adds mild emphasis, but contributes no syntax, format, or matching semantics beyond the schema. Baseline 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Names the resource (company profile), the authoritative source (French state registry), and enumerates the returned identity fields (SIREN, legal form, NAF, headcount, HQ, VAT, creation, status). It is distinguishable from company-search or company-kyb by being a consolidated registry-identity lookup, though it never explicitly contrasts itself with those siblings.

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

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

No statement of when to use this tool versus the many company-* siblings (company-search, company-financial, company-kyb, company-international-profile). The only usage hint is the '?siren=' parameter mention, which is a calling detail, not routing guidance.

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

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

No annotations are provided, so the description carries the full burden. It does disclose a meaningful behavioral trait: the paid access model ($0.005 per call, x402/USDC), which an agent needs to know before invoking. It does not cover auth, rate limits, or the shape of results, leaving gaps for a no-annotation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Four short clauses, front-loaded with the verb and resource; the funnel position and price each earn their place. The embedded '?q=name' pseudo-parameter and bracketed price are slightly noisy but do not bury the main point.

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

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

Covers purpose, positioning, and cost, but with no output schema and no annotations the description should say more about what a match list contains (SIREN, name, status), pagination, and what the 'top'/'results' parameters control. Nested-object params make this gap more consequential.

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

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

Schema coverage is nominally 100%, but the schema descriptions are only raw examples, so baseline 3 applies. The description's hint '?q=name' adds a query-by-name cue but is syntactically inconsistent with the schema property named 'query', and it says nothing about 'top' or 'results'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific verb and resource ('find French companies by name or SIREN') and enumerates the fields returned (identity, activity, status). The 'French' qualifier implicitly distinguishes it from company-international-search, though no sibling is named explicitly.

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

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

The phrase 'The entry point to the company funnel' implies this is the first step before profile/financial/KYB lookups, which is useful positional guidance, but there is no explicit when-to-use statement and no when-not or named alternative.

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

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

With no annotations, the description carries the full behavioral burden. It does disclose the transport ('GET'), that no parameters are needed, the upstream source (CoinMarketCap Pro), and the x402/USDC price of $0.010 per call, which is genuinely useful pre-invocation context. It says nothing about rate limits, freshness/latency of the snapshot, or failure modes.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

One dense sentence plus a bracketed pricing note; the resource and metric list are front-loaded and nothing is wasted. The pricing is separated into its own bracket so it does not dilute the core purpose.

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

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

For a no-param, no-output-schema snapshot tool with no annotations, the description substitutes for a return schema by naming the four returned fields, which is the right call. What remains missing is freshness/latency of the global snapshot and any indication of response structure, but the essentials for correct invocation are present.

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

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

Schema description coverage is 100% and the tool is explicitly parameterless, so the schema already carries the semantics. The description's 'GET (no params)' usefully confirms that the nested 'params' object with dominance/mcap/volume examples is not caller-supplied input, which is a real clarification given the confusing schema, but it adds no syntax or format detail beyond that.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific resource ('global crypto market snapshot') and enumerates the exact metrics returned: total market cap, BTC/ETH dominance, 24h volume, and a trend assessment. That is well beyond a tautology. It does not, however, distinguish itself from siblings like crypto-market, crypto-brief, or crypto-fear-greed, so it stops short of a 5.

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

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

There is no statement of when to use this tool versus the many adjacent crypto tools (crypto-market, crypto-brief, crypto-fear-greed, crypto-movers). The only routing-like signal is 'GET (no params)', which says how to call it, not when. An agent must infer the use case from the metric list alone.

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

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

With no annotations, the description carries the full behavioral burden. It usefully discloses the upstream source (CoinMarketCap Pro), that low-liquidity tokens are filtered out, and the paid call model ($0.020 per call, x402/USDC), but it omits error behavior, rate limits, and pagination/return characteristics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

It is a single dense sentence that front-loads the resource and scope, then adds source and pricing. Nothing is repeated, though the trailing GET/limit/price fragments read like appended metadata rather than a clean structure.

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

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

No output schema or annotations exist, so the description must do more. It covers the source, filtering intent, and cost, but leaves parameter usage ambiguous and mismatched with the schema, and says nothing about response shape — adequate but with clear gaps.

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

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

The description advertises a 'limit' query parameter, yet the schema exposes top/losers/gainers inside a nested params object — a mismatch that actively confuses rather than clarifies. Even with nominal 100% schema coverage, the inner schema fields are documented only by unhelpful example strings, so the description fails to make the parameters usable.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific resource and scope: top gainers and losers over 24h with a volume filter, returning prices and % changes. It is clear what the tool produces, but it does not distinguish itself from similar siblings like crypto-market, crypto-screener, or crypto-price.

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

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-to-use guidance, and no sibling is named as an alternative. Usage is only implied by words like 'movers' and 'gainers and losers'; an agent must guess whether this or crypto-market/crypto-screener is the right call.

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

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

With no annotations, the description carries the full burden. It does disclose meaningful traits: a fixed universe of six assets, USD-only denomination, and a per-call price of $0.010 via x402/USDC, which is genuinely useful for an agent deciding whether to spend. However it says nothing about error behavior, freshness/latency, or response shape beyond the field list.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with the asset list and returned fields, followed by the two constraint tags. Slightly dense but every clause carries information; no filler.

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

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

No output schema exists, so the description is largely responsible for return context; it does list the categories returned (prices, 24h change, breadth, top movers), which partially compensates. It still leaves the agent unsure which parameters actually exist/do anything and how this differs from the many crypto siblings.

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

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

There is one optional 'params' object with 100% schema description coverage, so the schema baseline of 3 applies. The description adds no meaning about what the params control — notably, the schema's 'market'/'prices' examples look like output fragments rather than inputs, and the description does nothing to clarify that.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('real-time snapshot of 6 major crypto assets') and enumerates the covered assets plus the values returned (24h changes, breadth, top movers). It does not name or distinguish itself from near-identical siblings such as crypto-market, crypto-movers, or crypto-global, so an agent cannot automatically route between them.

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

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

The description says what the tool is but never says when to pick it over crypto-market, crypto-movers, crypto-global, or crypto-historical. Only the constraints 'USD only' and the pricing note are given; no conditions for use, no exclusions, no alternative named.

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

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

With no annotations, the description carries the full burden and does disclose meaningful traits: the official upstream source (RTE éCO2mix), quarter-hourly data freshness, and a per-call price with payment rails (x402/USDC). It omits any auth/account prerequisites and error/failure behavior, which would be useful for a paid endpoint.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single dense line that is front-loaded with what the tool returns, then calling convention, then price. Every clause earns its place, though the packing of source, fields, freshness, method and billing into one sentence is borderline overloaded.

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

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

With no annotations and no output schema, the description compensates by enumerating returned measures and stating data source, freshness and cost. It is nearly complete for invocation, missing only auth/error expectations and any hint of scoping versus the neighboring energy tools.

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

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

Schema description coverage is 100% and the only real parameter is a nested 'params' object, so the schema already carries the documentation. The description adds the date format (YYYY-MM-DD) and that it defaults to today, which is genuinely useful but does not go beyond that.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (retrieve live demand) and resource (French electricity demand from RTE éCO2mix) and enumerates the payload fields (consumption MW, forecast, CO2 g/kWh, generation mix). It does not name or contrast with the obvious sibling energy-grid-status, so an agent must infer the distinction itself.

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

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

It gives the calling convention for the date parameter and its default (today), which implies when the tool is relevant, but it never says when to prefer this over energy-grid-status, energy-price, or the other energy-* siblings. Usage is implied rather than stated.

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

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

With no annotations at all, the description carries the full burden and does well: it discloses the paid cost ($0.020/call, x402/USDC), the data source (RTE éCO2mix official), the return unit (MW), and the sign semantics (positive = import). It omits auth mechanics and error behavior, which keeps it short of a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single dense line, front-loaded with resource, source and scope, then the call syntax and price. No filler; every clause carries information an agent needs.

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

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

No output schema and no annotations, so the description must stand alone; it covers the data domain, unit, sign convention, date default and cost, which is nearly everything needed to call it. It does not describe the exact response shape or field list, leaving a small gap.

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

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

Schema coverage is nominally 100% but the per-field descriptions are bare examples (e.g. "2026-10-02"), so the prose earns credit by clarifying the date format YYYY-MM-DD, the today default, and the positive-means-import meaning of net_import_mw. The 'borders' field is not explained in prose, keeping it below 5.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource — live French cross-border power exchanges — and names the official source (RTE éCO2mix). It enumerates the borders covered and the sign convention, which cleanly separates it from siblings like energy-grid-status or energy-demand.

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

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

The description implies context (live cross-border flows, date defaults to today) but never states when to use this instead of the many energy-* siblings (energy-brief, energy-price, energy-grid-status). Usage is inferable from the resource but no alternatives or exclusions are given.

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

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

With no annotations, the description carries the full burden. It usefully adds that the data is real-time, lightweight, sourced from awattar, and priced per call, but it does not describe permissions, rate limits, return format, or failure behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loads the resource and market. The brand prefix and bracketed cost note are slightly extra, but overall every phrase contributes useful information.

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

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

For a simple, zero-required-parameter read tool with no output schema and no annotations, the description identifies the returned data, source, region, and cost. However, it leaves usage routing and parameter semantics ambiguous, which matters given the large sibling set.

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

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

Schema coverage is reported at 100%, so the baseline is 3. The description adds EUR/MWh and Europe/awattar context, but it does not clarify the region values, whether price_eur_mwh and change_24h_pct are inputs or outputs, or how to use the nested params object.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Names the specific resource — current electricity spot price in EUR/MWh with 24h trend and hourly profile — and scopes it to Europe/awattar. It is clear what the tool returns, but it does not explicitly distinguish itself from many energy-* siblings such as energy-brief, energy-demand, or energy-solar-forecast.

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

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

The description says nothing about when to use this tool versus alternatives. There is no when-to-use, when-not-to-use, or named sibling guidance despite a crowded energy toolset.

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

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

With no annotations, the description carries the full burden and does disclose key operational traits: live Coinbase-sourced data, the HTTP GET method, and crucially the paid access model ($0.020 per call, x402/USDC), which an agent must know before invoking. It stops short of documenting rate limits, error behavior, or freshness/latency guarantees.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The content is dense and front-loaded, but it is a single run-on marketing sentence that repeats the tool name and mixes purpose, returns, and pricing without structural separation. Nothing is wasted, yet readability suffers.

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

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

There is no output schema and no annotations, so the description appropriately compensates by enumerating the returned fields (spot, spread, fees, slippage, verdict) and the payment requirement. It is nearly complete, though it leaves the confusing nested input schema unaddressed and omits any failure/latency expectations.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds a usage example (?currency=EUR) that clarifies the only true input parameter and implicitly disambiguates the misleading schema fields (best_rail, coinbase_spot, rail_fees_usd) that read like response values. It does not add new semantic depth beyond the example.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb+resource: fetching the best fiat↔USDC conversion rate per payment rail (Base, Polygon, Solana). It enumerates the composed outputs (Coinbase spot EUR/USD, bid-ask spread, network fees, slippage hint, best-rail verdict), which makes it easy to distinguish from generic siblings like crypto-price or crypto-market.

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

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

Usage is implied by 'for agents settling in USDC' and the explicit GET form (?currency=EUR), but there is no explicit when-to-use vs. when-not, and no alternative sibling (e.g. payment-routing-advice) is named for comparison. The agent must infer the appropriate context.

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

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

No annotations exist, so the description carries the full burden. It usefully discloses the data source (open Wikidata) and the payment requirement (price $0.020 per call, x402/USDC), which is real behavioral context. It omits rate limits, error/no-match behavior, and whether any param combination narrows results vs. returns a list.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single dense, front-loaded sentence covering resource, outputs, source, and invocation example, followed by the price note. Slightly run-on in the field enumeration, but little waste.

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

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

With no output schema and no annotations, the description does the work of naming return fields, source, and cost, which is adequate for a lookup tool. However, it ignores four of the five declared inputs, so an agent cannot tell whether iata/icao/coord/country are alternate lookup modes or refinements of the q query.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds the useful fact that q accepts either a name or an IATA code, but it never mentions the schema's other inputs (iata, icao, coord, country), so it does not enrich semantics beyond the q parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific resource (airport brief) and enumerates the returned fields (country, IATA/ICAO, coordinates, elevation, runway count, airlines hint), so the agent knows exactly what it gets. It does not explicitly differentiate from sibling flight-airport-situation or other flight-* tools, leaving the reference-vs-live distinction to inference.

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

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

Provides concrete invocation examples (?q=Jakarta or ?q=CGK) that imply how to call it. No statement of when to prefer this over flight-airport-situation, flight-route-risk, or notam-area, and no conditions/exclusions are given.

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

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

With no annotations, the description carries the burden and does disclose useful non-schema traits: the upstream source (ClinicalTrials.gov v2), the cost model ($0.020 per call) and payment rail (x402/USDC). It omits rate limits, pagination behavior, and result format, so it is not exhaustive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the source and search facets, followed by the returned fields, a concrete GET example, and the price. Dense but nothing is wasted; the single run-on sentence is slightly less scannable than a structured list would be.

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

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

With no output schema or annotations, the description still enumerates returned content (status, phase, eligibility criteria, contacts) and query mechanics, which is close to sufficient. Missing details on exact response shape and limits keep it from being fully self-contained.

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

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

Schema description coverage is 100% but the documented params are thin (example strings only). The description meaningfully extends this by naming real query parameters (condition, drug, status, limit) and sample values, giving the agent more to work with than the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (search) and resource (worldwide clinical trials from ClinicalTrials.gov v2) and names the searchable facets: condition, drug, sponsor. The domain is distinct from health-* siblings, though no sibling is explicitly named or excluded.

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

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

Gives clear triggering context ('search by condition, drug or sponsor') and a concrete example query with optional filters (drug, status=recruiting, limit). No when-not-to-use guidance or named alternatives (e.g. health-medical-literature) is provided.

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

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

With no annotations, the description carries the full burden; it usefully discloses the return shape (titles, links, snippets), the source set, and the per-call cost/payment rail (x402/USDC), which an agent genuinely needs. It does not cover rate limits, error behavior, or any free-tier/auth nuance beyond the price tag.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence plus a bracketed price note; dense but no filler, and the resource and scope come first. The em-dash chain is slightly crammed but still readable.

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

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

For a no-annotation, no-output-schema search tool, the description covers the return fields, source scope, and cost, which is what an agent needs to call it correctly. Only the `results` and `sources` parameters are left entirely to the schema.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds an example value for `q` ("licenciement") but says nothing about the `results` limit or `sources` filter beyond what the schema already shows.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific verb+resource (legal watch by keyword, French official sources) and enumerates the covered domains (labor law, contracts, regulations), so an agent can immediately tell it apart from news-eu, economy-news-fr, or company-news. It stops short of naming a sibling alternative, so 4 rather than 5.

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

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

Usage is implied by 'search results … GET ?q=licenciement', which shows the invocation pattern, but there is no explicit when-to-use / when-not guidance or pointer to an alternative for broader or non-French legal coverage.

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

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

With no annotations, the description carries the full burden and does disclose real behavior: the importance threshold, cross-feed deduplication, reinforced verification, a 2-hour refresh cadence, and the $0.020 per-call charge via x402/USDC. That pricing and cadence disclosure is genuinely useful. It still omits auth requirements, failure modes, and return shape, keeping it short of a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single tight paragraph with the core scope front-loaded before the supporting details. The 'executive briefing for busy agents' line is mild marketing filler, but the sentence still earns its place as a use-case cue.

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

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

For a zero-annotation, no-output-schema tool this covers cadence, filter level, dedup behavior, and price — a reasonable minimum. It never explains what a briefing item contains (topic/headline/source) or how the nested params object should be formed, so an agent still has gaps to fill.

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

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

Schema description coverage is reported at 100%, so the baseline is 3. The description adds nothing about the single 'params' object — its shape is only conveyed by example strings in the schema, so the description neither compensates nor detracts.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('World News Briefing'), the aggregation scope (all 7 Xynaptic news feeds), and the filtering rule (importance >= medium). It implicitly distinguishes itself from the regional siblings (news-us, news-eu, news-asia) by being global, but never names them, so differentiation is left to inference.

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

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

The phrase 'executive briefing for busy agents' and the 2-hour refresh hint at when this is appropriate, and the importance>=medium threshold implies it is the high-signal choice. However there is no explicit statement of when to pick this over news-us/news-eu/news-tech or any exclusion of use cases.

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

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

With no annotations, the description carries the full burden, and it does add real context: the official FAA NMS-API data source, the returned formats, and an explicit cost of $0.020 per call via x402/USDC. It does not disclose auth/settlement requirements beyond the price tag, rate limits, or how failures/empty results behave, so meaningful gaps remain.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single dense block that front-loads what the tool is, then source, example request, and pricing. Every clause carries information, though the marketing-style 'Xynaptic' prefix and bracketed price tag add a little noise.

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

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

For a no-annotation, no-output-schema fetch tool, the description covers source, content shape, code format, example call, and cost – enough to invoke it correctly. The main omission is any routing information relative to notam-area.

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

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

Schema description coverage is 100%, though the field descriptions are only literal examples (location: "KDFW", notams: 12, critical: 2). The description's 'GET ?location=KDFW' conveys the location format (ICAO or 3-letter) and improves on that, but the numeric notams/critical params remain unexplained in both places, so it neither compensates nor regresses.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb+resource (official FAA NOTAM briefs for US airports) and enumerates the content returned (runway/taxiway/airspace notices, effective dates, plain-English and ICAO formats). It is clear what the tool does, but it never distinguishes itself from the sibling notam-area, which an agent could easily confuse it with.

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

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

There is no when-to-use or when-not-to-use guidance, and no alternative is named despite notam-area being an obvious overlapping sibling. The agent is left to infer that this is the per-airport NOTAM lookup from the example GET query alone.

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

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

No annotations exist, so the description carries the full burden and does reasonably well: it discloses the data provenance (real DVF transactions), the delivered statistics (median, mean, min/max, volume), a 'robust median' caveat, and a per-call price with the x402/USDC payment mechanism. It omits auth prerequisites beyond payment, rate limits, and error/edge-case behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Compact and front-loaded: purpose first, then method/provenance, then a concrete call example, then pricing last. Every clause earns its place with no padding.

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

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

With no output schema and no annotations, the description compensates by naming the returned statistics and the pricing model, which is enough for a simple lookup. It leaves minor gaps around valid city/type values and the stray median_price_m2 parameter, but an agent has what it needs to invoke it.

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

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

Schema description coverage is 100%, so the baseline is 3. The description's example (?city=Paris&type=Appartement) shows intended values, adding mild value, but it never explains the schema's unexpected 'median_price_m2' query parameter, and no enum constraints clarify the valid city/type domains.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource: median price per sqm for a French commune and property type, with a named data source (DVF) and returned statistics. Clear enough to distinguish a median-price lookup from most siblings, but it never names or contrasts the adjacent tools (property-valuation, property-market, property-compare), so differentiation is left to inference.

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

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

The description shows a sample call ('GET ?city=Paris&type=Appartement') but gives no when-to-use, when-not, or alternative guidance. An agent cannot tell from this text why it should pick property-price over property-valuation or property-market.

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

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

With no annotations, the description carries the full burden and discloses meaningful behavioral traits: the endpoint is a GET (implying a safe read-only operation), data is refreshed intraday, up to 30 days of OHLCV history are available, and each call costs $0.010 via x402/USDC. It does not spell out rate limits or error behavior, but for a read-only market-data endpoint the cost, refresh cadence, and HTTP method are the most decision-relevant details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, dense sentence with the tool name and primary purpose front-loaded, followed by returned fields, request format, refresh note, and cost. Every clause contributes relevant information, though the bracket notation for pricing is slightly compressed rather than fully structured.

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

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

With no output schema or annotations, the description does the heavy lifting by listing the returned data fields, specifying the history range, noting intraday refreshes, and stating the call cost and payment protocol. It omits error handling and rate limits, but for a spot price and short-history endpoint the description provides enough for an agent to invoke it correctly.

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

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

Schema coverage is 100%, but the nested parameter descriptions are only example values, not semantic documentation. The description adds value by showing the exact query format (GET ?ticker=AAPL&history=30) and introducing a history parameter not present in the schema, clarifying that up to 30 days of OHLCV data can be requested.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names the specific resource (spot market data for US stocks) and enumerates the exact fields returned: price, previous close, day range, 52-week range, currency, exchange, and up to 30d OHLCV history. It distinguishes itself from siblings like security-eu-price (EU scope) and security-technical (technical analysis vs raw spot data) through the explicit 'US stocks' and 'spot market data' framing.

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

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

The description defines what the tool provides and includes a concrete request example (?ticker=AAPL&history=30), which implies usage for fetching US stock spot prices and short-term history. However, it does not explicitly state when to prefer this over alternatives such as security-technical, security-eu-price, or security-fundamentals, leaving the agent to infer the scope from the field list.

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

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

Without annotations, the description carries the behavioral burden. It discloses returned fields (object, family, department, publication date, BOAMP link), an example query string, and the per-call price with payment protocol (x402/USDC). Missing are rate limits, pagination behavior, and error handling, but core operational context is present.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately front-loaded with tool name and purpose, but includes a few marketing-oriented sentences ('Sell to whom? Track...') that could be trimmed. Overall it remains compact and readable for its information density.

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

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

Given the absence of annotations and output schema, the description covers the essential return fields, query format, and cost. It lacks pagination details, rate limits, and auth mechanism specifics, but for a simple paid search tool it is largely complete.

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

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

The input schema lists a single 'params' object with q, total, and notices, which does not match the actual query parameters. The description compensates by showing the real GET parameters (q, buyer, dept, days, limit) and an example URL, adding substantial meaning beyond the schema's example-only descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (search) and resource (French public procurement notices from BOAMP), and distinguishes itself from generic search tools by naming the domain and data source. An agent can identify it as the tender-search tool without opening the schema.

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

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

Provides clear use-case context ('Track your clients tenders, find business, monitor competitors') so the agent understands the intended scenarios. It does not explicitly compare to alternatives or state when not to use it, but no misleading guidance is given.

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

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

With no annotations, the description carries the full burden and mostly succeeds: it discloses price ($0.500/call, x402/USDC), validity (90 days), the prepaid/no-account model, the auth header format (X-TICKET: ticket_id|ticket_secret), and that balance checks are free. It omits failure/refund/expiry behavior, so not quite complete.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loads the value proposition and return artifacts, then the usage mechanics, then the body format. Dense but every sentence carries information; slight clutter from the bracketed price tag and interleaved status-check note.

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

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

For a tool with no annotations and no output schema, it covers the essentials an agent needs: cost, purchase amount, what is returned, how to spend the ticket, lifetime, and where to check balance. Missing edge cases (failed payment, expired ticket) keep it from a 5.

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

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

The schema's body object has no documented properties, so the description is the only source for the key parameter: amount_usd with a 0.5–20 range and 'your choice in amount_usd'. It adds real meaning beyond the schema, though it says nothing about the optional params object.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('pay once ..., get {ticket_id, ticket_secret}') and names what the ticket enables. It is clearly distinguishable from the sibling ticket-status, which it explicitly relegates to balance checks.

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

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

Explains when to use it (consume ANY Xynaptic service at 20% off with no per-call payment) and routes balance checking to POST /v1/ticket-status, a sibling. It does not state when not to buy a ticket (e.g. one-off vs bulk), which keeps it below a 5.

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

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

With no annotations, the description carries the full behavioral burden. It usefully discloses pricing ('$0.030 per call, x402/USDC'), which is real behavioral context, but says nothing about auth flow, rate limits, error handling, or caching behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Dense but front-loaded: purpose first, then returned fields, then invocation example, then price. Every clause roughly earns its place, though the description packs a lot into one run-on line.

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

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

No output schema exists, so the description reasonably compensates by listing departure/arrival times, duration, transfers, modes, CO2, and best-journey pick. Combined with the noted payment model, an agent has enough to invoke it, though missing usage routing keeps it short of complete.

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

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

Schema coverage is 100%, so the nested params object already documents each field with examples; baseline is 3. The description adds the GET query syntax including date/time not present in the schema, which is mild extra value but also introduces an inconsistency worth noting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific verb+resource+scope ('full train itinerary between two French stations or cities (SNCF/Navitia)') and enumerates the returned data. An agent can distinguish it from rail-trip-brief or rail-departures without opening a schema.

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

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

The example GET query implies usage, but there is no explicit when-to-use guidance or when-not, and no routing to close siblings like rail-trip-brief, rail-intermodal-compare, or ticket-buy. Usage is inferable from the domain but not stated.

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