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

No annotations exist, so the description carries the full disclosure burden, and it delivers little beyond the topic: nothing on result ordering, deduplication across the four databases, rate limits, whether PDFs come back as links or text, or what happens when a source fails. The only behavioral hint is 'open-access PDFs', which implies returned fields but does not guarantee them.

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 sentence, front-loaded with the verb and the resource, with zero filler. It reads slightly like a keyword list, but every item maps to something the tool actually returns or searches.

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 four-parameter aggregation tool with no output schema and no annotations, the description should explain how results from OpenAlex, Semantic Scholar, Crossref, and arXiv are combined or ranked, and whether citation-velocity data is always available. None of that appears, and the missing patent scope from the name compounds the 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 description coverage is 100%, so the baseline is 3 and the schema already explains query, minYear, sources, and maxResults. The description adds no syntax guidance (e.g., how 'sources' interacts with availability, or whether citation velocity requires a specific backend), so it 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?

The description gives a specific verb ('Search') plus concrete resources ('peer-reviewed scientific papers', 'citation velocity', 'DOI metadata', 'open-access PDFs') and names the four backends. An agent can tell it is a scholarly-literature search tool. It misses a 5 because the tool name advertises 'patent research' yet the description never mentions patents, leaving a scope mismatch an agent could trip over.

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 only implied: the phrasing implies general literature discovery, but there is no statement of when to prefer this over another approach, no prerequisites, and no exclusions. With no closely related siblings in the list, the absence of routing guidance is less damaging, but no explicit 'use this when...' clause is present.

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 supplied, so the description carries the full behavioral burden, and it only partially does so: it enumerates what data comes back (useful given there is no output schema) but says nothing about whether this is a read-only lookup, expected latency, rate limits, credit cost, or what happens when a domain cannot be enriched. The absence of any operational caveat for a multi-source network enrichment call is a real gap.

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 zero filler; every enumerated item is substantive output information. It is dense and slightly list-heavy (the '38+' figure is marketing-adjacent), but nothing could be cut without losing signal.

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 four parameters, no annotations, and no output schema, the description compensates by naming the returned data categories, which is the main thing an agent needs. It still omits response shape (per-domain vs. batched records), failure behavior for unenrichable domains, and any cost/latency expectation, leaving it adequate rather than 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 description coverage is 100%, so the schema already defines all four parameters, and the baseline is 3. The description adds only indirect hints — the '38+ technology stack fingerprints' and 'MX/SPF/DMARC' phrases loosely signal what detectTechStack and extractContacts toggle — without adding format, batching, or cost semantics beyond the schema.

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?

Uses a specific verb ('Enrich') on a specific resource ('B2B company domains') and enumerates the exact data classes produced (emails, phones, social profiles, tech stack, MX/SPF/DMARC, RDAP). The purpose is unmistakable and no sibling tool in the list covers B2B domain enrichment, so confusion is unlikely even though no sibling is named.

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 is a pure capability list with no when-to-use framing, no prerequisites (e.g., domains must be company root domains, not URLs), and no guidance on when to disable detectTechStack or extractContacts to save time/cost. An agent must infer the call context entirely from the name.

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 behavioral burden, and it discloses almost nothing. It does not say whether extraction is read-only, whether results are paginated, how listing categories are selected (the description mentions three verticals but the schema has no category parameter), or what a returned listing contains.

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 no filler or redundancy. It is efficient, but its brevity comes at the cost of the usage and behavior detail noted elsewhere, and it explicitly names three verticals the schema cannot target.

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, no output schema, and a scraping tool whose results and limits matter, the description should at least state the return shape, pagination or cap behavior, and what omitting the query yields. None of that is present, so an agent cannot predict the response.

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 both parameters (query, maxItems) are already documented in the schema, establishing a baseline of 3. The description adds no usage nuance, such as what happens when query is omitted even though it defaults to 'macbook'.

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 ('Extract'), the resource ('marketplace, automotive, and real estate listings'), and the exact source ('Bolha.com in Slovenia'). The site/country reference implicitly separates it from the Croatia, Serbia, Bosnia and Balkan classifieds siblings, though it never names 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?

There is no when-to-use, when-not-to-use, or alternative guidance at all. An agent must infer from the tool name that this is the Slovenia-specific classifieds scraper and that the sibling scrapers cover other countries, with no confirmation in the description.

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 supplied, the description must carry the behavioral burden, and it does disclose useful traits: the data is "live," it reads Level-2 depth, and it computes slippage at three fixed notional sizes. It does not say whether spreads are net of fees, how fresh the data is, whether any exchange requires keys or has rate limits, or whether the call is read-only and side-effect free — meaningful gaps for a market-data scanner.

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 no filler: the verb comes first, followed by the three analytical outputs and then the venue scope. Nothing is repeated from the schema verbatim and no sentence is wasted.

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 three-parameter, all-optional tool with no output schema, the description conveys what analysis the caller gets back (depth, tiered slippage, arbitrage spreads) and over which venues. It falls short of fully explaining the default behavior when symbols/exchanges are omitted, and it does not sketch the shape of the returned records, which matters since no output schema exists to do that job.

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; the description earns an extra point because it enumerates the supported exchange universe (Binance, Bybit, Kraken, Coinbase, OKX, KuCoin), whereas the schema's exchanges field is an unconstrained string array with only four defaults and no enum. It adds less for symbols and nothing for maxResults, and it does not explain the fixed slippage tiers as behavior tied to output.

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 uses a specific verb ("Analyze") tied to three concrete resources: Level-2 order book depth, execution slippage at $10k/$50k/$100k tiers, and cross-exchange arbitrage spreads. It also enumerates the exact venues (Binance, Bybit, Kraken, Coinbase, OKX, KuCoin), which cleanly separates it from sibling tools like turkey_crypto_exchange_prices_monitor that also touch crypto prices.

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 intended use case is strongly implied by the phrase "cross-exchange arbitrage spreads" and the slippage tiers, so an agent can infer this is the tool for depth/liquidity/arbitrage questions. However, there is no explicit when-to-use statement, no exclusion of cases (e.g., single-venue price lookup), and no pointer to any alternative tool, leaving selection to inference.

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 and it is thin: it does not state that this is a read-only aggregation of external feeds, mention API/auth requirements, rate limits, caching or data freshness for the KEV/EPSS/CVSS sources, or describe how the correlation is ranked. Naming the three upstream data sources gives some behavioral context, but key operational traits are absent.

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 no filler or repetition. It is efficient, though it could have traded a few of the source acronyms for one clause on behavior or scope.

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 four-parameter scanner with no output schema and no annotations, the description covers the 'what' but not the 'how' — return shape, ranking method, source freshness, and the unexplained 'specific_cves' mode all remain unaddressed. Adequate as a minimum-viable definition, but an agent cannot fully predict the result or the mode-specific behavior.

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 all four parameters already carry their own descriptions, which sets the baseline at 3. The tool description adds no meaning about how mode, minEpssScore, vendorFilter, or maxResults interact — notably it never explains that 'specific_cves' mode would need a CVE identifier, for which no parameter exists.

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 (correlate) and three concrete resources (CISA KEV, FIRST.org 30-day EPSS, NVD CVSS v3) with the stated goal of DevSecOps prioritization. No sibling tool in the list covers vulnerability data, so differentiation is implicit rather than needing to be spelled out. It stops short of saying what the tool actually returns (a ranked CVE list, a scan report, etc.).

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 only implied through the phrase 'for DevSecOps prioritization'; there is no explicit when-to-use, when-not-to-use, or prerequisite guidance. No alternative tool is named, though no sibling overlaps functionally. The agent must infer that this is for vulnerability triage based on the intent phrase 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 are supplied, so the description carries the full behavioral burden. It never states that this is a read-only data query, whether any authentication or API key is needed, how fresh the DeFiLlama data is, whether results are paginated or truncated by maxResults, or what the response looks like for each of the three modes — 'live ... intelligence' is the only hint, and it is vague.

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 that names the capability set and the data source with no filler or repetition. Every clause earns its place by identifying a distinct monitoring domain.

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 4-parameter, all-optional tool with no output schema and no annotations, the description covers what domains exist but omits what happens with defaults, how each mode's output differs, and the read-only/safety profile. It is adequate to attempt a first call but leaves meaningful 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?

Schema description coverage is 100%, so the schema already documents mode, chains, minTvlUsd, and maxResults, making 3 the baseline. The description adds only indirect meaning by naming the three data domains that correspond to the mode values; it adds no syntax, default behavior, or filtering semantics beyond the schema.

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 pairs the verb 'Monitor' with three concrete resources — yield farming APY pools, multi-chain protocol TVL, and stablecoin peg health — and names the data source (DeFiLlama), which is far more specific than the sibling crypto tools like crypto_orderbook_arbitrage_scanner or dex_token_scanner_security_auditor. It stops short of explicitly differentiating itself from those crypto siblings, so a 4 rather than 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?

Usage is only implied: the three domains map to the three 'mode' values, so an agent can infer when this tool fits, but there is no explicit when-to-use or when-not-to-use guidance and no named alternative such as the DEX security or orderbook scanners. 'Monitor' also suggests recurring/polling use without stating it.

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 supplied, the description carries the full disclosure burden. It helpfully reveals the two upstream data sources and the specific checks performed, which implies a read-only audit rather than a mutation. However, it omits failure behavior (e.g., tokens or chains GoPlus does not support returning empty/unknown results), rate limits, and whether findings are per-pair or per-token aggregates.

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 with zero filler, front-loaded with the verb and resource before the chain list and detection checks. It slightly overstuffs one sentence rather than splitting capability from usage, but nothing is wasted.

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 4-parameter, all-optional tool with no output schema and no annotations, the description explains what is scanned and what is detected but not what an audited pair record contains (field names, risk flags, unknown/missing handling). It is adequate to invoke, but the agent cannot anticipate the result shape or how to interpret a token with unscannable contract data.

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 every parameter already carries a documented meaning and default. The description only reinforces the chain list already present in the chainId enum-like documentation and adds no detail on how searchQuery accepts symbols versus contract/pair addresses, so the baseline 3 applies.

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 ("Scan DEX tokens"), enumerates the five supported chains, names the underlying data sources (DexScreener, GoPlus), and lists the exact risks detected (honeypots, mint/freeze authority, buy/sell taxes, liquidity locks). This clearly separates it from siblings like multichain_wallet_aml_risk_screener (wallet-level AML) and crypto_orderbook_arbitrage_scanner (price arbitrage).

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. The agent must infer the use case (pre-trade token due diligence) from the list of detected risks; nothing states the conditions under which this tool should be chosen over the AML or arbitrage scanners.

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 full burden, and it does disclose two behavioral traits beyond the schema: real-time freshness and cross-network verification of events. It does not say how cross-verification manifests in results (merged vs. duplicate entries), how results are ordered, or how the networks are reconciled when they disagree.

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 well-formed sentence with the resource, scope, and sources front-loaded and no filler. Slight deduction because "cross-verified" is an undefined term that is doing a lot of unstated work.

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 two-optional-parameter read tool with a fully documented schema and no output schema, the description covers what it is and where data comes from. What is missing is minor but real: nothing on result ordering, deduplication across networks, or whether an empty cross-verification result means no events or no consensus.

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 both parameters (maxResults, minMagnitude) are already documented in the schema, establishing a baseline of 3. The description adds no meaning about how thresholds interact across the three feeds (e.g., whether a single network's magnitude suffices) and does not mention the 4.5 default.

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 ("Track") and resource ("seismic events"), plus the three data sources (USGS, EMSC, AFAD) and the "cross-verified" aggregation angle. This implicitly separates it from single-network siblings like turkey_earthquakes_afad_official_feed and europe_mediterranean_earthquakes_emsc, though it never names them 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?

Usage is only implied: the "global and regional" + multi-network framing suggests this is the broad aggregator versus regional single-source siblings. There is no explicit when-to-use, when-not-to-use, or named alternative, leaving the agent to infer the routing rule from sibling names.

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 supplied, so the description carries the full disclosure burden. It says 'real-time', which is the one genuinely useful behavioral hint, but it says nothing about rate limits, authentication, result ordering, truncation behavior, or whether data is cached versus live, leaving a mutation-free but still underspecified read 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?

A single tight sentence with no filler, and the region and real-time nature are front-loaded where an agent scanning names and descriptions will catch them. Nothing is redundant, though nothing is elaborated either.

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 two-parameter read tool with a fully documented schema and no output schema, the description covers the essentials of what it retrieves. What it omits — differentiation from the sibling EMSC/USGS/afad monitor and any indication of result ordering or coverage limits — are the gaps that matter most here.

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 both maxResults and minMagnitude are already documented with defaults, and the baseline of 3 applies. The description's mention of magnitudes and depths loosely reinforces minMagnitude but adds no format, units, or filtering semantics beyond the schema.

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?

Specific verb 'Fetch' with a precisely scoped resource: real-time European and Mediterranean seismic events, plus the fields returned (epicenter coordinates, depths, magnitudes) and the EMSC source. It is distinguishable by region, though the sibling earthquake_monitor_usgs_emsc_afad also draws on EMSC, and the description never acknowledges that overlap.

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 when-to-use or when-not-to-use guidance at all. With both earthquake_monitor_usgs_emsc_afad and turkey_earthquakes_afad_official_feed among the siblings, an agent gets no signal about why it should pick this regional EMSC feed over those alternatives.

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 of behavioral disclosure. It does not mention whether the operation is read-only, whether authentication or rate limits apply, how results are paginated, or what happens on failure, leaving significant transparency gaps.

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 front-loaded sentence with no wasted words. It efficiently communicates the search target, relevant data types, and output links.

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 straightforward two-parameter search tool, the description is adequate but incomplete. It mentions notices and official PDF/HTML links but does not describe result behavior, pagination, or any operational constraints, which are not covered elsewhere since there are no annotations or output 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 both parameters already have clear schema-level documentation. The description adds some context by mentioning CPV codes and contracting authorities, but it does not add meaning beyond what the schema already states about searchQuery and maxResults.

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 opens with a specific verb, 'Search,' and precisely identifies the resource: European Union Tenders Electronic Daily (TED) public procurement notices. It also lists relevant searchable content such as CPV codes, contracting authorities, and official document links, making the tool's function unmistakable.

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 the tool is for EU public tender searches, but it does not state when to use it versus alternatives or clarify any prerequisites. With no closely related sibling tools, the usage context is reasonably implied, but explicit guidance is absent.

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 and does not meet it. It never says the operation is read-only, does not mention openFDA rate limits or pagination, and does not disclose that results are limited to the default category unless overridden.

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 front-loaded sentence with no filler, and the source ('official US openFDA') plus the resource are stated first. It is efficient, though brevity here shades into under-specification rather than true completeness.

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, no output schema, and a defaulted category parameter, the description says nothing about what a report contains (classification, reason, distribution) or that omitting category silently returns drug recalls only. An agent could call it correctly but would misjudge the returned scope.

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 schema already documents both parameters, establishing a baseline of 3. The description's enumeration of drug, device, and food reinforces the category values but adds no syntax, default behavior, or result-cap semantics beyond the schema.

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 ('Query') and a specific resource ('official US openFDA drug, medical device, and food recall enforcement reports'), which is distinguishable from the nearby fda_warning_letters_compliance_scraper and global_product_recalls_monitor. It does not name those siblings explicitly, so an agent must infer the boundary rather than read it.

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 mention of the two closest alternatives (global product recalls, FDA warning letters). The word 'official' hints at authoritative-source preference, but the agent gets no routing criteria.

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 must carry the full behavioral burden, but it only states the data domain. It does not disclose freshness, pagination, rate limits, authentication needs, whether results are summaries or full letters, or what 'monitor' means operationally for a single tool invocation.

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 definition is a single front-loaded sentence with no wasted wording. Its weakness is under-specification rather than verbosity.

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 no annotations, no output schema, and a similar sibling tool for FDA recalls, the description is too thin for an agent to invoke it confidently. It omits return behavior, data freshness, pagination, and sibling routing guidance.

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 both parameters are already documented in the schema. The description does not add meaning about maxResults limits or how searchKeyword matches company names versus violation keywords, so baseline 3 is appropriate.

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: US FDA warning letters covering pharmaceutical, medical device, biologics, and food GMP inspections, plus violation categories. The verb 'Monitor' is less precise than 'scrape' or 'list' for a callable tool, and it does not distinguish this from siblings such as fda_recalls_enforcement_monitor.

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 guidance on when to use this tool versus alternatives, especially versus fda_recalls_enforcement_monitor or global_product_recalls_monitor. The description implies a monitoring use case but gives no conditions, exclusions, or prerequisite 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 are provided, so the description carries the full behavioral burden, yet it only describes what is searched. It does not disclose whether the operation is read-only, how results are paginated, any rate limits or scraping limitations, or what the returned listing details include.

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 front-loaded sentence with no wasted wording. It is appropriately concise, though its brevity contributes to gaps in behavioral and usage context.

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?

The tool is simple, with three optional parameters and full schema coverage, and the description identifies the official source and result types. However, with no annotations and no output schema, it leaves out important context about return format, pagination behavior, and operational limitations.

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 all three parameters are documented in the schema with defaults and examples. The description does not add parameter meaning beyond the schema, so the baseline score of 3 is appropriate.

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 and resource: search official German Federal Employment Agency job vacancies, employers, and locations. It is clear enough to distinguish the domain from most sibling tools, though it does not explicitly contrast itself with any alternative.

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 guidance on when to use this tool versus alternatives, no prerequisites or exclusions, and no mention of whether it is intended for German job-market searches only. The intended use is implied by the official agency name, but no explicit usage rules 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?

No annotations are provided, so the description carries the full behavioral burden, yet it says nothing about whether results are a live feed or a historical snapshot, how fresh the data is, or whether pagination exists. It also omits what a returned alert contains (product name, hazard, date, recall number). Only the coverage scope is disclosed.

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 zero filler; the scope-defining sources are placed immediately after the verb. It is efficient, though its brevity comes at the cost of the routing and behavior information an agent would want.

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?

The tool is simple (2 optional params, no nested objects), but there is no output schema, so the description should indicate what a result looks like and whether the feed is current. It also fails to resolve the overlap with the FDA recall sibling. For an unannotated read tool with no return-shape documentation, this is thin.

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 both parameters are already documented in the schema, including the source identifiers (cpsc, fsis, eu) and the maxResults cap. The description adds no syntax, default behavior, or filtering nuance beyond that. Baseline 3 applies when the schema does the heavy lifting.

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 clear verb+resource ("Monitor consumer product safety recalls") and names the three specific authorities it covers (CPSC, FSIS, RAPEX/Safety Gate). An agent can tell it is a recall feed rather than a general compliance tool. It stops short of explicitly differentiating itself from the close sibling fda_recalls_enforcement_monitor, which covers a different regulator domain.

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 obvious alternative, fda_recalls_enforcement_monitor, or the fda_warning_letters_compliance_scraper sibling. Nothing says whether this is for consumer-product hazards only versus food/drug enforcement, leaving the agent to infer the boundary. No exclusions or prerequisites 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?

No annotations are supplied, so the description carries the full burden. It does disclose the underlying data sources being queried, which is genuinely useful behavioral context, but it says nothing about whether the operation is read-only, whether it requires credentials or API keys for the sanctions/PEP sources, what rate limits apply, or how matches are scored and disambiguated (false positives are the central risk in screening).

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 that names the verb, the subjects, and the three data sources with no filler. It is efficient, though the comma-separated list of sources is somewhat dense and could be structured as a short bulleted scope statement.

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 four parameters, no output schema, and no annotations, the definition should do more to describe what a returned 'compliance record' contains and how match confidence is conveyed. It adequately identifies what the tool queries but leaves the shape and interpretation of results entirely unspecified.

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 all four parameters are already documented in the schema. The description adds only marginal meaning by mapping the tool's data sources to the two boolean toggles (GLEIF LEI and sanctions matching), without explaining format expectations for entity strings or what maxResults truncation means for compliance completeness.

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 uses a specific verb ('Screen') plus specific resources (GLEIF LEI, OFAC/UN/EU sanctions lists, PEP databases) and states the subject scope (corporate entities and individuals). This distinguishes it clearly from the nearest sibling, multichain_wallet_aml_risk_screener, which screens wallets rather than legal entities. It stops short of explicitly naming that sibling, so it lands at 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?

The phrase 'for KYB/AML compliance' and the inclusion of 'corporate entities and individuals' implies the domain, but there is no explicit statement of when to use this tool, when not to, or which alternative to pick. An agent must infer from the name and sibling list whether entity screening or wallet screening is appropriate.

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 behavioral burden. It doesn't state whether scraping requires auth or proxies, whether requests can be rate-limited or blocked, how pagination works, or how the category string maps to site sections. Only the extraction domain is disclosed.

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 no filler or wasted words. It is quite terse for a scraper with behavioral unknowns, but structurally sound.

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 scraping tool with zero annotations, no output schema, and only two loosely described parameters, the description omits critical operating details: return shape, listing fields, pagination, anti-bot limitations, and category path format. Completeness is inadequate relative to the tool's operational complexity.

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 both parameters are already documented in the schema; baseline 3 applies. The description adds marginal context by naming content domains that hint at valid category values, but it doesn't give concrete category path syntax such as the 'nekretnine' default.

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 ('Extract') and resource (real estate, apartments, automotive classifieds) scoped to a named site, HaloOglasi.com in Serbia. This distinguishes it from sibling Balkan classifieds scrapers (pazar3_merrjep, njuskalo, bolha, olx_bosnia, kupujemprodajem) by site and country, though the category enumeration is slightly redundant since apartments fall under real estate.

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 guidance on when to use this tool versus the many sibling classifieds scrapers, and no mention of prerequisites, exclusions, or pagination limits. Usage is only implied by the site name embedded in the tool name and description.

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 behavioral burden. It says what is calculated but does not disclose calculation method, timezone handling, date-range behavior, return format, or whether the operation is purely read-only.

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 definition is a single front-loaded sentence with no wasted wording. It efficiently names the calculation, the prayer fields, Qibla direction, and the geographic scope.

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?

The description is adequate for a simple city/country lookup, but it claims daily and monthly calendars while the schema exposes no date or month parameter. With no output schema and no annotations, more detail about date handling and return behavior would be needed for full completeness.

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 city and country parameters are already well documented. The description adds the global-city scope but does not add syntax, default behavior, or date-related parameter meaning beyond the schema.

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 uses a specific verb, 'Calculate,' and names the exact resource: Islamic prayer times and Qibla direction. It lists the prayer fields and scope ('any global city'), so an agent can immediately distinguish this from unrelated sibling tools.

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 purpose implies when to use it, but there is no explicit guidance on when to choose daily versus monthly output, how dates are selected, or what defaults apply. No sibling tool is a direct alternative, so the lack of alternatives is less harmful, but usage remains 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?

No annotations exist, so the description carries the full behavioral burden, yet it discloses nothing about scraping limits, pagination, result ordering, rate limiting, or whether prices are localized/currency-formatted. It does at least enumerate the extracted fields (listings, prices, seller details), which is the only return-value signal since no output schema exists.

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 no filler or redundancy; the source domain and extracted fields are stated up front. It is arguably too terse for a tool with zero annotation coverage, but nothing in it is wasted.

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 low-complexity two-optional-parameter scraper with full schema coverage and no output schema, the description covers what the tool returns at a high level. It leaves gaps around pagination, coverage limits, and why one would pick this site scraper over the other Balkan classifieds 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%, so both parameters are already documented in the schema and the baseline of 3 applies. The description adds no meaning beyond that — notably it does not explain that 'keywords' defaults to 'iphone' or that 'maxItems' governs result truncation.

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 pairs specific verbs ('Search and extract') with a concrete resource set ('product listings, prices, and seller details') and names the exact source site, KupujemProdajem.com in Serbia. That site naming implicitly separates it from adjacent Balkan classifieds siblings such as halooglasi_serbia_classifieds_scraper, though no sibling is called out 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?

There is no guidance on when to use this versus the other Serbia/Balkan classifieds scrapers (halooglasi, pazar3_merrjep, olx_bosnia, bolha, njuskalo), which is exactly the ambiguity an agent faces here. Usage is only implied by the word 'Search'.

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 supplied, so the description carries the full behavioral burden and mostly doesn't: it implies a read-only lookup but never states permissions, API key or subscription requirements, rate limits, latency for multi-address batches, or cost per address. Nothing is said about what happens with an unsupported chain in the chains array or how many addresses can be screened in one call.

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 zero filler; the screening dimensions are packed into one readable clause list. Slightly dense but nothing needs removing.

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 should explain the shape of the result — how risk scores are expressed, whether flags are per-chain or per-address, how balances/velocity are returned — and it does none of that. For a multi-chain scoring tool with three parameters and no structured return definition, this leaves the agent guessing about what it will receive.

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% (addresses, chains with enumerated values and default, maxTransactions with default), so the schema does the parameter work. The description only gestures at chain scope via "EVM and Solana" and adds no format guidance (address normalization, checksum, mixed-chain arrays), which is the baseline-3 expectation.

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?

"Screen EVM and Solana wallet addresses" is a specific verb plus resource, and the follow-on list (native balances, transaction velocity, mixer exposure, sanctions flags, AML risk scores) tells the agent exactly what signals come back. It does not name or differentiate itself against siblings such as global_sanctions_pep_gleif_kyb_screener or dex_token_scanner_security_auditor, 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 this tool is appropriate versus the sanctions/PEP screener sibling or the token security auditor, and no exclusions (e.g., non-EVM chains, contract addresses vs EOAs). The agent must infer usage entirely from the verb and 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?

With no annotations, the description carries the full behavioral burden and discloses almost nothing beyond the fact that it scrapes listings. It does not say whether results are live or cached, whether there are rate limits or throttling, whether pagination exists, or what fields a listing contains, which matters for a scraper hitting an external site.

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 zero filler that leads with the action and the source. It is efficient but so terse that it omits any structure for usage or return 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 two-parameter, no-required-args tool with no output schema, the description covers the essential 'what'. However, it leaves out return shape, listing count behavior, and any operational constraints, leaving the agent to discover them by calling.

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% with only two parameters, both documented in the schema itself (query default 'iphone', maxItems default 25). The description adds no meaning about keyword matching behavior or what maxItems counts against, so the baseline 3 applies.

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?

Uses a specific verb ('Extract') plus resource ('marketplace, automotive, and real estate listings') and names the exact source site and country, so the agent can tell it apart from the Balkan classifieds siblings (pazar3, bolha, olx_bosnia, halooglasi) by site alone. It stops short of explicitly stating how it differs from those siblings, which keeps it from 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 when-to-use or when-not-to-use guidance, no mention of alternatives, and no prerequisites such as geo-restrictions or needing a non-default query. The only routing signal is the site name embedded in the purpose statement, which the agent must infer.

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 supplied, so the description carries the full behavioral burden, yet it says nothing about rate limits, authentication, pagination, or result freshness. 'Extract' implies a read-only scrape, but that is inference rather than disclosure, and there is no mention of what happens when a query returns nothing or how many pages are traversed.

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 zero redundant wording. Every clause (categories, source, country) carries distinct information, and nothing is buried or repeated.

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 only two optional parameters this is a simple tool, but it has no output schema and no annotations, so the description should explain what a returned listing contains (price, location, seller, URL) and whether results are capped or paginated. Instead it stops at the sourcing statement, leaving the agent to discover output shape empirically.

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%, with both query and maxItems fully documented in the schema, so the baseline is 3. The description adds no parameter-level meaning and in fact introduces a gap: it promises three listing categories, but the schema exposes no category filter, leaving unclear whether 'query' drives category selection or whether all categories are always returned.

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 (extract), a specific resource (listings), named categories (vehicles, real estate, consumer goods), a named source (OLX.ba) and geography (Bosnia and Herzegovina). Against siblings like pazar3_merrjep_balkan_classifieds_scraper, njuskalo_croatia_classifieds_scraper, and bolha_slovenia_classifieds_scraper, the domain name alone effectively disambiguates, even 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?

There is no when-to-use guidance and no exclusions — nothing tells an agent when to prefer this over the other Balkan classifieds scrapers, or whether it is appropriate only for Bosnia-scoped queries. Usage must be inferred entirely from the site name.

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 supplied, so the description carries the full behavioral burden, and it discloses almost nothing beyond the target sites. It does not say whether results are live-scraped or cached, how pagination or item caps behave beyond the maxItems default, whether the sources can fail or rate-limit, or what fields a listing record contains. 'Scraper' implies a read-only harvest, but that is never stated.

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 well-formed sentence with the geography and category scope front-loaded and no filler. It is efficient, though the brevity is partly a function of how little is said rather than disciplined editing of a richer description.

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 scraping tool with no annotations and no output schema, the description leaves the agent without any picture of the return shape, per-source reliability, or item-limit behavior. All three parameters are documented, but everything behavioral is absent, which is a substantial gap given structured fields provide no cover.

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 each of the three parameters carries a default and a description, so the schema already does the parameter work. The description's mention of 'real estate, vehicles, and marketplace listings' loosely echoes the category values but adds no format, valid-range, or interaction detail beyond the schema. Baseline 3 is appropriate.

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 verb (extract) and a specific resource (real estate, vehicles, marketplace listings) and enumerates the three countries and source sites covered, which geography-plus-category framing distinguishes it from the Serbia, Croatia, Slovenia and Bosnia classifieds scrapers. It stops short of naming an adjacent sibling such as turkey_real_estate_listings_scraper, so the boundary is inferable rather than explicit.

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, no statement that it should be preferred over the other national classifieds scrapers for these three markets, and no mention of prerequisites or limits. The agent has to infer applicability entirely from the country 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?

With no annotations, the description carries the full behavioral burden. It does disclose that results are real-time and that cross-venue arbitrage is computed across three exchanges, which is genuine value. However it says nothing about API-key requirements, per-venue rate limits, failure behavior when a venue is unreachable, or whether arbitrage detection is cross-venue or intra-venue — significant gaps for a multi-source aggregation 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?

A single front-loaded sentence that names the verb and then enumerates the returned metrics and venues without filler. It is dense but readable; the only deduction is that the venue list at the end could be trimmed since the sources parameter already enumerates them.

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 four optional parameters, 100% schema coverage, and no output schema, the description's enumeration of returned fields (probabilities, odds, volume, liquidity, arbitrage) usefully compensates for the absent output schema. It falls short only on operational context such as availability of the venues and the shape/limits of the response.

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 all four parameters (keyword, sources, maxResults, minVolumeUsd) are already documented, establishing a baseline of 3. The description adds only indirect meaning — it confirms the venue list and implies volume/liquidity are first-class fields — but does not explain how keyword matching works, whether sources accepts partial names, or how minVolumeUsd is measured.

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 concrete verb ('Fetch') and a concrete resource (real-time implied probabilities, yes/no odds, volume, liquidity, arbitrage) and names the three venues. The purpose is unmistakable and no sibling tool covers prediction markets, though the description never explicitly contrasts itself with alternatives. Minor inconsistency: the tool name advertises only Polymarket/Kalshi while the description correctly adds Manifold.

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 guidance, no conditions under which an agent should prefer this over a general market data tool, and no explanation of what 'arbitrage opportunities' means in this context. The only implicit cue is 'real-time', which suggests freshness-sensitive tasks but gives no exclusions or prerequisites.

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 at all, so the description carries the full behavioral burden, and it discloses little beyond 'Fetch'. Undisclosed: that all three parameters are optional with baked-in defaults (calling with an empty object returns AAPL/NVDA filings rather than everything), whether the tool respects SEC EDGAR rate limits, and what the response contains. 'Fetch' does imply a read-only operation, which is the only behavioral signal 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?

A single front-loaded sentence with the scope (official SEC EDGAR), the form types, and the accepted keys all in one pass. No filler, no restatement of the name.

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, the description should explain return shape and result limits, and it should warn that defaults quietly scope the query. It states neither, so an agent cannot predict that an empty call yields a two-ticker, three-form sample rather than a broad filing search.

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 each parameter's meaning is already documented in the schema; the description adds little beyond restating ticker/CIK/form-type targeting. The one extra hint is that a bare form number like '4' corresponds to Form 4 insider transactions, which slightly clarifies the schema's terse '4' 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?

Specific verb (Fetch) plus a precisely named resource (official US SEC EDGAR corporate filings) and an enumeration of form types (10-K, 10-Q, 8-K, Form 4 insider transactions). It also states the three lookup keys (ticker, CIK, form type). Sibling differentiation is implicit only because no other sibling covers SEC filings, 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?

The phrase 'by ticker, CIK, or form type' implies how the tool is driven, but there is no explicit when-to-use/when-not guidance, no note about the shipped defaults (AAPL/NVDA, 10-K/10-Q/8-K) that apply when the caller passes nothing, and no alternative suggested for other financial-data needs.

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 behavioral burden. It discloses the shape of what is extracted (catalog, variants, stock, compare-at price, discount %), which implies a read-only public-data scrape, but it says nothing about rate limits, failure behavior on invalid or non-Shopify domains, or whether results are paginated versus truncated by the max caps.

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 that lists the extracted fields and the target scope with no filler. Every clause corresponds to real capability or output content an agent would want to know before calling.

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 usefully enumerates the returned data (catalog, variants, stock, compare-at prices, discounts), which compensates for that gap, and the input schema is fully self-documenting. What is missing is behavioral context for a scraper: domain validity requirements and result-cap semantics.

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 all five parameters are already documented with defaults and examples, setting the baseline at 3. The description adds only the broad domain scope; it does not clarify how maxResults and maxProductsPerStore interact, or how searchKeyword matches titles versus tags.

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 uses a specific verb ('Extract') tied to a concrete resource set ('complete product catalogs, SKU variants, stock availability, compare-at prices, discount percentages') and a bounded scope ('any global Shopify store domain'). No sibling tool targets Shopify storefronts, so an agent can distinguish it from the classifieds/tenders/recalls scrapers by domain alone.

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 only implied: 'any global Shopify store domain' tells the agent this works on Shopify-powered storefronts, which is a useful implicit precondition. There is no explicit when-to-use/when-not guidance, no mention of what happens with a non-Shopify domain, and no statement about the alternative tools for non-Shopify e-commerce data.

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 of behavioral disclosure. It says nothing about scraping limits, rate limits, freshness/caching of data, cost, or whether results are bounded by maxEntriesPerTopic defaults. That it is a scraper implies live network fetching and possible latency/partial-result behavior, none of which is disclosed.

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 no filler or redundancy. It is efficient, though its brevity is driven by under-specification rather than disciplined scoping.

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 scraping tool with no annotations and no output schema, the description should say what source is scraped, what the returned fields look like, and any limits or failure modes. It instead gives only a list of data categories, leaving the agent unable to predict the response shape or the scraping target.

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 both parameters are already fully documented in the schema. The description's mention of trending topics and entries loosely maps to includeTrending and maxEntriesPerTopic but adds no syntax, format, or edge-case meaning beyond that. Baseline 3 is appropriate.

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 clear verb ("Extract") and concrete outputs (trending topics, user entries, favorite counts, sentiment), which is more specific than the name alone. However, it never identifies which community/forum is being scraped, so the actual target resource is left ambiguous even though the tool name implies a forum. Siblings are all unrelated domains, so no differentiation is needed, but the missing source leaves the purpose only partially resolved.

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, when not to, or what alternatives exist. No prerequisites, no mention of whether it requires a forum identifier or works against a fixed default source. The agent gets a capability statement and nothing about selection or 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 are provided, so the description carries the burden of behavioral disclosure. It reveals the data sources and the general discovery purpose, but it does not state whether results are read-only, how "high-traction" is determined, whether sources are combined or queried separately, or whether there are rate limits or freshness constraints.

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, front-loaded sentence that states the core purpose and sources without unnecessary wording. Every element contributes to identifying what the tool does.

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?

The description identifies the domain and source coverage, but with no annotations and no output schema, it does not explain the result format, ranking behavior, or how multi-source results are structured. It is adequate for a simple optional-parameter discovery tool but leaves meaningful behavioral context 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%, and each parameter is already documented in the schema. The description reinforces the available sources but adds no extra meaning for the keyword or maxResults parameters, so it meets the baseline for high schema coverage without exceeding it.

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 uses a specific verb, "Discover," and names the exact resources: high-traction startups and open-source developer tools across Y Combinator, GitHub Trending, and Hacker News launches. This clearly separates the tool from the unrelated sibling tools, so an agent can identify its domain without ambiguity.

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 gives clear usage context by naming the three deal-flow sources and the type of discovery it supports. It does not include explicit exclusions or say when to prefer another tool, but no sibling tool covers this startup/developer-tool discovery use case, so the missing alternatives are not a major gap.

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 it does disclose the meaningful data categories returned (buying, selling, bank-note, cross rates), which is real behavioral content. However, it says nothing about publication cadence beyond 'daily', what happens on weekends/holidays, whether values are indicative or settlement rates, or any auth/rate-limit constraints.

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 no filler, and the most discriminating detail (official TCMB source and rate categories) comes first. It is efficiently sized, though the same budget could have carried one clause of usage or return-format context.

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 one-optional-parameter read tool with no output schema, the definition covers what the tool fetches but leaves return shape, freshness semantics, and the currency scope (TRY base) unstated. Adequate to select the tool, thin for invoking and interpreting it confidently.

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% for the single optional `currencies` array with its default, so the schema already carries the parameter meaning. The description adds nothing about the parameter (e.g., whether unsupported codes error out or are ignored), so the baseline 3 applies.

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 (fetch) and a precise resource (TCMB daily FX buying, selling, bank-note, and cross rates). The 'official Central Bank (TCMB)' qualifier separates it from market-data siblings such as turkey_gold_and_bank_bullion_prices and turkey_crypto_exchange_prices_monitor. It stops short of stating the base currency (TRY) or the date coverage, so it is clear but not fully distinguishing.

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 guidance on when to use this tool versus the gold, crypto, or fuel price siblings, and no mention of exclusions such as historical lookups or non-TCMB sources. Usage is only implied by the word 'official' and the FX asset class.

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 behavioral burden, and it only enumerates returned fields. It does not say whether results are live-scraped or cached, whether prices are real-time or indicative, whether any authentication or rate limit applies, or how a departure date is determined when the only input is a route pair — a significant gap for a schedule/price lookup.

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 front-loaded sentence that enumerates the extracted fields with no filler, which is efficient for a scraper-style tool. It reads slightly like a keyword list rather than prose, but every element names a real output dimension.

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 one-optional-parameter tool with full schema coverage, the definition covers what is collected and in what currency. It is still thin on operationally important context: there is no output schema and the description does not indicate result shape, date/departure handling, or data freshness.

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 single 'routes' property already documents its format with examples ('istanbul-ankara') and a default. The prose adds no meaning beyond that — nothing about direction sensitivity (istanbul-ankara vs ankara-istanbul), multi-route batching behavior, or naming conventions for cities.

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?

A specific verb ('Extract') paired with a well-scoped resource: intercity bus schedules, operators, terminals, seat configurations, and TRY fares across Turkey. The domain is distinct from nearby siblings such as turkey_domestic_flight_prices_aggregator and turkey_greek_islands_ferry_schedules, so an agent can tell what it retrieves, though the description never names those alternatives 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?

Usage is implied by the domain ('intercity bus ... across Turkey'), which lets an agent infer this is the ground-transport option versus flight/ferry siblings. However, there is no explicit when-to-use/when-not guidance, no mention that multiple routes can be passed, and no statement about whether dates matter.

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 behavioral burden. It does disclose the dimensions being compared (daily prices, fleet classes, transmission, supplier ratings) and that coverage spans airports and cities, but says nothing about data freshness, supplier coverage, or whether multiple locations can be compared in one call.

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 sentence with no filler, front-loaded with the comparison verb and followed by the data dimensions and geographic scope. Every clause contributes 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 one-parameter tool with no annotations and no output schema, the description conveys what is compared and where, but leaves the agent unaware of the result structure and whether results are per-location or aggregated. It is minimally sufficient rather than 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?

There is a single optional parameter with 100% schema description coverage, including examples of valid location values, so the schema already does the work. The description adds no syntax, defaults, or multi-location behavior beyond what the schema states, making the baseline of 3 appropriate.

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 and resource: comparing daily car rental fleet prices, vehicle classes, transmission types, and supplier ratings across airports and cities. This is clearly distinguishable from every sibling tool, none of which cover car rentals, so an agent can select it 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?

Usage is only implied by the word 'Compare' and the mention of airports and cities; there is no explicit statement of when to use this tool versus alternatives, nor any exclusions or prerequisites. Adequate for a narrow, single-purpose tool but no real 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 supplied, so the description carries the full burden. It does disclose the data dimensions collected (pair quotes, 24h volume, order book spreads) and the exchange coverage (BtcTurk, Paribu), which is real behavioral value, but it never states freshness/real-time behavior, snapshot vs. streaming semantics, rate limits, or whether exchange coverage is exhaustive. That leaves meaningful gaps for a monitoring tool with zero annotation support.

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 that names the venue, the currency pairs, the data dimensions, and the exchanges, with no filler. It is dense but well ordered; splitting out the exchange list would marginally improve scanability.

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 should do more about return shape and data freshness. It adequately answers 'what data' and 'which exchanges', but not 'how current', 'how many results per exchange', or what a ticker record contains — gaps that matter for a monitoring/scraping 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 description coverage is 100%, so both maxResults and quoteCurrency are already documented, and the description only echoes the TRY/USDT framing that corresponds to quoteCurrency. It adds no format or behavior detail (e.g., how ALL differs from TRY, or how many tickers exist per exchange) beyond the schema.

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 uses a specific verb ('Monitor') plus concrete resources: TRY/USDT trading pairs, 24h volume, and order book spreads, and names the covered venues (BtcTurk, Paribu). That makes it clearly distinguishable from sibling tools like crypto_orderbook_arbitrage_scanner or tcmb_official_central_bank_fx_rates, even 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?

There is no when-to-use guidance, no statement of what this tool is not for, and no pointer to alternatives such as the arbitrage scanner. The only implied usage cue is 'Monitor', which suggests live market data rather than historical prices, but that is left for the agent to infer.

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, yet it says nothing about whether fares are live or cached, what currency they are in, whether one-way or round-trip is assumed, or how many results come back. Most notably it advertises departure/arrival times while the schema exposes no date parameter at all, leaving the temporal behavior of the query unexplained.

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 no filler, and the compared attributes are listed compactly. It is appropriately sized, though the brevity is partly under-specification rather than tight editing.

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?

The enumeration of return fields (fares, times, baggage, carriers) partially substitutes for the missing output schema, which is a real strength for a two-parameter tool. However, with no annotations and no output schema, an agent still lacks the query-time context (date of travel, currency, result limits) needed to invoke it confidently.

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%, with both origin and destination documented including example slugs and defaults, so the baseline is 3. The description adds nothing about slug resolution (city vs airport code) or whether 'regional' implies non-Turkish destinations, so it does not go beyond the schema.

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 uses a specific verb ('Compare') with a concrete resource ('domestic and regional airline ticket fares') and enumerates the compared dimensions (fares, times, baggage, carriers), which lets an agent separate it from ground-transport siblings like turkey_bus_routes_and_ticket_prices or turkey_greek_islands_ferry_schedules. The only softness is the word 'regional', which leaves the covered route set somewhat open.

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, no named alternative, and no mention of route scoping or that both parameters are optional with defaults of istanbul/izmir. The comparative framing implies a fare-shopping use case, but the agent must infer everything else.

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 supplied, so the description carries the full burden. It does convey that the source is official AFAD and real-time, and 'Retrieve' implies a read-only fetch, but it says nothing about refresh cadence, geographic boundary for 'neighboring regions', result ordering, or rate/access constraints.

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 no filler and the source qualifier ('official AFAD') placed early. It is well sized for the amount of information it actually delivers, though the brevity contributes to the gaps elsewhere.

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 two-parameter read-only feed with a fully documented schema and no output schema, this is close to adequate. However, the undefined 'neighboring regions' scope and the lack of any routing against the overlapping earthquake siblings leave real ambiguity for an agent.

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 both parameters ('maxResults', 'minMagnitude') are fully described in the schema, so the baseline of 3 applies. The description adds no interpretation of what magnitudes mean, defaults, or sensible ranges beyond what the schema already states.

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') plus a named resource ('official AFAD real-time earthquake records') with an explicit geographic scope ('Turkey and neighboring regions'). That distinguishes it from generic scrapers, but it never differentiates itself from the sibling 'earthquake_monitor_usgs_emsc_afad', which appears to overlap substantially.

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 when-to-use, when-not-to-use, or alternative-selection guidance is given. With two obvious sibling earthquake tools ('earthquake_monitor_usgs_emsc_afad', 'europe_mediterranean_earthquakes_emsc'), an agent has to guess which feed is appropriate for a given query.

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 supplied, so the description carries the full disclosure burden and largely does not meet it. It implies a read-only retrieval and daily refresh, but says nothing about currency/units, whether prices are a current snapshot or time series, whether data is scraped from official sources (latency, freshness), or any rate limits.

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 front-loaded sentence with no filler; the resource, product list, and geographic scope all appear before any agent decision point. Nothing is repeated from the tool name that isn't clarifying.

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 single-parameter read tool with no output schema, the definition covers what is fetched but not what comes back — no currency, unit (per liter/ gallon), field shape, or time dimension. Adequate to select the tool, thin for interpreting results.

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 single `provinces` parameter is fully documented in the schema, so baseline 3 applies. The description does not mention the parameter at all, and its phrase 'across all 81 provinces' sits slightly at odds with the schema default of only istanbul, ankara, and izmir, which could mislead an agent into thinking full coverage is the default.

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 ('Retrieve') and a precise resource ('daily retail gasoline, diesel, and autogas (LPG) pump prices') with an explicit geographic scope ('all 81 provinces'). It is unmistakable against the nearest siblings (turkey_gold_and_bank_bullion_prices, turkey_crypto_exchange_prices_monitor) because it names the exact fuel products, though it never explicitly contrasts itself with 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?

There is no when-to-use or when-not-to-use guidance, no mention of alternative tools, and no stated preconditions. The adverb 'daily' implies a cadence but the agent is left to infer whether this returns today's snapshot only or a historical series.

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 supplied, so the description carries the full behavioral burden. It discloses that prices are 'live' and that spreads span 35 banks, but says nothing about refresh cadence, whether the call requires auth or has rate limits, or how stale data can get — meaningful gaps for a real-time market-data fetch.

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 sentence, front-loaded with the primary action and resource, followed by the secondary bank-comparison scope. There is no filler or repetition, and the enumeration of instruments is information-dense rather than verbose.

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 read-only fetch with one optional boolean and no output schema, the description covers the domain, the instruments returned, and the bank-spread dimension. It stops short of describing the response shape or data freshness, which a caller would still want to know.

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 single parameter's own description already explains the 35-bank spread comparison, so the baseline is 3. The description restates the same bank/spread concept in prose, adding reinforcement but no new semantics such as what happens when includeBanks is false.

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 ('Fetch') plus a specific resource ('Turkish gold prices') and enumerates the exact instruments covered (Gram, Quarter, Half, Republic, Ons) and the bank-spread comparison. This clearly separates it from siblings like turkey_crypto_exchange_prices_monitor or tcmb_official_central_bank_fx_rates without the agent needing to open any 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?

Usage is implied by the domain ('live Turkish gold prices'), so an agent can infer when it is relevant, but there is no explicit when-to-use/when-not guidance and no named alternative for related needs such as FX rates or crypto prices. Adequate but leaves routing to inference.

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 and largely drops it: nothing is said about whether prices are live or cached, how fresh timetables are, whether the operator list is exhaustive, or what happens with an unknown route slug. Only the implied read-only nature of 'Fetch' gives any behavioral signal.

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 sentence, front-loaded with the verb and the returned data categories, with no padding or redundancy. It is efficient, though it could have spent a few more words on coverage limits or defaults at little cost.

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?

The tool is simple (one optional array parameter, no output schema), so the bar is low, but the description never explains that omitting routes falls back to the default 'yenikapi-mudanya' route or what the response shape contains per route. Adequate to select the tool, thin on how to call it well.

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 schema already documents the routes array, its item type, its default, and gives example slugs. The description adds nothing about the parameter, so the baseline of 3 applies.

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 ('Fetch') and resource ('maritime ferry timetables, operators, sailing durations, and ticket prices') scoped by geography ('Aegean, Marmara, and Greek Island routes'). This clearly distinguishes it from transport siblings like turkey_bus_routes_and_ticket_prices and turkey_domestic_flight_prices_aggregator, though it does not name 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?

No when-to-use guidance, no when-not-to-use exclusions, and no mention of the nearby alternatives (bus routes, domestic flights) an agent might confuse it with. The agent must infer that this applies to sea crossings only.

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 behavioral burden. It does disclose the returned data dimensions (rates, review scores, stars, board types, amenities), which helps an agent judge relevance, but it says nothing about source, data freshness, whether results are live-scraped or cached, rate limits, or failure behavior. For a scraping-style read tool with zero annotation coverage, that leaves meaningful gaps.

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 that enumerates the extracted fields with no filler or repetition. Every clause contributes information about what the tool returns.

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?

The tool is simple (two optional params, no output schema), and the description usefully enumerates the return dimensions in place of an output schema. However, for a pricing tool it omits currency, whether rates are per-night per-room, occupancy assumptions, and data freshness/source — all material for interpreting results. 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?

Schema coverage is 100%: both parameters have descriptions, defaults, and an example list (istanbul, antalya, bodrum) for cities. The description adds only the implicit notion of multi-city targeting via "across Turkish destinations," which is already clear from the schema. Baseline 3 is appropriate when the schema does the heavy lifting.

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 verb ("Extract") plus a concrete resource set (nightly rates, guest review scores, star ratings, board types, amenities) scoped to Turkish destinations. An agent can immediately tell this is hotel data collection, which is distinct from every listed sibling (flights, car rental, classifieds, FX, etc.). It stops short of naming or contrasting any sibling, so it lands at 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?

There is no when-to-use guidance, no when-not-to-use, and no named alternative. The agent must infer the use case (Turkish hotel price/quality research) purely from the resource list. Nothing indicates what to do instead if the caller wants flights, rentals, or non-Turkish hotels.

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 that results are daily emergency on-duty pharmacies and include addresses, phone numbers, and coordinates, but it does not explain data source, refresh timing, coverage completeness, or error behavior for invalid city/district values.

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, front-loaded sentence with no superfluous wording. Every element—resource, return fields, and geographic scope—earns its place.

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 two-parameter lookup with no output schema, the description is largely complete because it names the key returned fields and clarifies the resource. It stops short of describing data freshness, source, or default city behavior, which would make it 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%, so the input schema already documents the city and district parameters. The description's phrase 'across provinces and districts' mirrors the schema without adding syntax, default behavior, or matching rules beyond what the schema provides.

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 uses a specific verb and resource: 'Find daily emergency on-duty pharmacies.' It also states the returned data types (addresses, phone numbers, map coordinates) and geographic scope, making the tool clearly distinct from the unrelated Turkey-focused 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?

Usage is implied by the resource name: an agent would use this to locate on-duty pharmacies. However, the description gives no explicit when-to-use guidance, no exclusions, and no mention of alternatives or limitations such as only current-day results.

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 supplied, so the description carries the full behavioral burden. It does usefully disclose the returned content (listings, square-meter pricing, agent details), which partially substitutes for a missing output schema, but says nothing about data freshness, source portals, error behavior for unsupported city slugs, or cost/rate limits for a scraping operation.

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 resource stated first and no wasted wording. It is tight and readable, though the brevity leaves no room for the usage or behavioral context that the absent annotations call for.

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?

All three optional parameters are fully documented in the schema and the description covers the extracted payload, so basic invocation is covered. However, for a scraper with no annotations and no output schema, an agent still lacks page/coverage limits, expected data shape, and failure modes for unknown cities or categories.

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 all three parameters (city, category, maxItems) are already documented with defaults and examples. The description adds only marginal framing — 'regional cities' reinforces the city scope and 'commercial' hints that category covers more than sale/rental — which does not exceed the schema baseline.

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 ('Extract') and specific resources (residential/commercial real estate listings, square-meter pricing, agent details) scoped to regional Turkish cities. An agent can tell exactly what domain this covers, and no sibling tool overlaps that domain, though the description never explicitly contrasts itself with 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?

There is no when-to-use guidance, no prerequisites, and no exclusions or alternatives named. The agent must infer from the name and description that this is the tool for Turkish property data, with nothing about when it should prefer another source or when it should not call it.

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 behavioral burden, yet it discloses almost nothing beyond the data scope. It does not state that the operation is a read-only fetch, whether province names are matched case-insensitively or must be exact, how unknown provinces are handled, rate limits, or what the observation and forecast payloads contain. 'Official' signals provenance but not 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?

One sentence that front-loads the source, the verb, and the two data products returned. There is no filler, hedging, or redundant restatement of the tool name.

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 single-parameter, no-output-schema tool this is close to adequate: the agent knows what data comes back and for which geography. However, with no annotations and no output schema, gaps remain around default behavior when provinces is omitted, data freshness/units, and return shape that the description could reasonably cover.

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 single optional parameter already documents the province list with examples and defaults, so the baseline of 3 applies. The description's 'across 81 provinces' confirms the accepted universe of values but adds no syntax, matching rules, or behavior for omitted or invalid entries.

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 (retrieve) and a clearly bounded resource: MGM current observations plus 5-day forecasts for Turkish provinces. The authoritative source and geographic scope (81 provinces) leave no ambiguity about what the tool returns. It does not need to differentiate from siblings since none of the listed tools cover weather, but it also does no explicit routing.

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 only implied: an agent can infer this is the tool for Turkish weather lookups. There is no statement of when to prefer it over another source, no prerequisites, and no note that the call defaults to Istanbul/Ankara/Izmir when provinces is omitted. An agent must infer the calling context entirely from the tool name and scope sentence.

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