agiscorecard

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

No annotations are provided, so the description carries the full burden. It discloses that it presents third-party public quotes with as-of times and explicitly states 'no bets, no affiliate links,' clarifying it is read-only and non-commercial. Error behavior is not described, but with zero parameters this is acceptable.

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, information-dense sentence followed by a page reference. It front-loads the board name and then lists specific data elements, with each phrase adding value and no wasted words.

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 parameters, the description fully specifies the data included (venues, horizons, median, spread, implied date, resolution basis, formula) and the source page. Nothing an agent needs to call it correctly is missing.

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 are zero parameters, so there is nothing for the description to add beyond the schema. Baseline of 4 applies because no parameter documentation is needed.

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 clearly identifies a specific resource (AGI prediction-market consensus) and enumerates venues (Polymarket, Kalshi, Manifold, Metaculus), horizons (2027-2040), and metrics (median, spread, implied 50% date, resolution basis). This distinguishes it from siblings like get_claim_ledger or search_site 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 rich context about what data is available, but it never explicitly states when to use this tool versus alternatives, nor does it mention any exclusions. Usage must be inferred from the name and content, which is adequate but not directly instructive.

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 provided, the description carries the full behavioral burden. It discloses read-only intent and a validation side-effect, and states the default target. However, it does not mention error behavior, output format, or the network implications of fetching an arbitrary URL, leaving some behavioral 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 compact, front-loaded with the core action, and every sentence earns its place: the resource definition, the parameter behavior, and the protocol spec link. No wasted words.

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 one optional parameter and no output schema, the description covers the key context: what a ledger is, what it contains, the default source, and how to target a custom ledger. It does not detail response structure or failure modes, but the embedded spec link helps fill those 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%, so the baseline is 3. The description adds value by clarifying the default behavior when the parameter is omitted ('returns the reference ledger') and by introducing the validation aspect of passing a URL, which goes beyond the schema's simple 'read another site's ledger' phrasing.

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 and resource — 'Read a Claim Ledger Protocol v0.1 ledger' — and further defines the resource's content (evidence tiers, dated verdict, flip condition). This makes the tool's purpose unambiguous and clearly distinct from the sibling tools, which target different record types.

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 explicit invocation guidance: omit arguments for the reference ledger, or pass a URL ending in /claimledger.json to read another site's ledger. It does not explicitly contrast with sibling tools or state when not to use this tool, so it stops short of a full 5.

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

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

With no annotations, the description carries the disclosure burden, and it does a good job: it flags that returns are priced on the FILING DATE rather than quarter end to reflect tradability, and it adds the 'never investment advice' caveat. It does not describe response format or pagination, but those are less critical for a no-parameter getter.

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

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

The description is a single dense sentence that front-loads the dataset identity and packs in relevant detail about scope, methodology, and the educational caveat. It is not flabby, though the long clause chain could be broken up for easier scanning.

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

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

For a no-parameter tool with no output schema, the description conveys the dataset's content and key pricing nuance, but it never states what the tool actually returns or in what shape. The phrase 'what copying them would have returned' is about hypothetical investment returns, not the tool's response, leaving a small ambiguity about output.

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

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

The input schema has no parameters and schema coverage is effectively complete at 100%, so parameter explanation is not needed. The baseline of 4 applies, and the description's dataset context adds value without needing to document params.

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 clearly identifies the Invest dataset and enumerates its contents: eight graded predictions mapped to 17 AI equities, eight investors' SEC 13F positions, and hypothetical filing-date returns. It distinguishes itself from siblings by focusing on the Invest domain, but it never states an explicit verb such as 'returns' or 'fetches', so the action is implied rather than stated.

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 explains what the dataset contains and adds an 'educational only' caveat, but it does not specify when to use this tool relative to siblings like get_thesis_tracker or get_verdicts, nor any exclusions. An agent must infer applicability solely from the name and content.

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?

Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world, so safety is covered. The description adds genuinely non-obvious context beyond that: the fixed 2026-10-02 NY close entry, the specific metrics returned (adjusted-close returns, drawdowns, excess SPY percentage points, entry prices, freshness, source links), and the 'No trading or return promises' disclaimer.

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

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

Four compact sentences, front-loaded with the resource and benchmarks before the metric list and the parameter hint. Every sentence carries information; the disclaimer sentence is the only one that leans slightly toward boilerplate but still sets a behavioral expectation.

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

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

There is no output schema, so the description correctly enumerates what comes back (returns, drawdowns, excess SPY points, entry prices, freshness, source links) and pins the entry date. It stops short of defining 'normalized daily series' or the response shape, but for a single-optional-parameter read-only tool this is nearly 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 0% for the single boolean parameter, so the description must carry the meaning — and it does, explaining that include_history yields a normalized daily series rather than just the summary. That is meaningful semantics the bare boolean/default=false schema does not convey.

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 ('Read') and a precisely scoped resource: the public twelve-stock model portfolio measured against SPY, QQQ and TQQQ. An agent immediately knows what data this returns and on what benchmark basis, which distinguishes it from the other portfolio/position 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 only implied — the agent infers it should call this to inspect model-portfolio performance. There is no explicit when-to-use or when-not-to-use guidance, nor any mention of the sibling tools (e.g., get_invest_positions) it might be confused with. The one instruction present ('Set include_history for normalized daily series') is parameter guidance, not tool-selection guidance.

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

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

No annotations are provided, so the description carries the full burden. It usefully discloses the semantic nature of the returned labels (not a win rate, not verified net return), which is real behavioral context, but says nothing about authentication, data freshness, coverage limits, or response shape.

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?

Two compact sentences, front-loaded with the resource identity and followed by the interpretive warning. No filler, though the phrasing is slightly dense and could be tightened.

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

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

For a zero-param tool with no output schema and no annotations, the description should carry more weight. It names the data categories but never characterizes the return structure or volume, leaving an agent guessing at what actually comes back.

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 tool takes zero parameters and the schema is empty with 100% coverage, so there is nothing for the description to disambiguate. Baseline 4 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 states the resource clearly: an editorial market-call ledger with outcome labels and evidence audit, tied to a specific domain. The retrieval verb is only implied by the name, and it does not contrast itself with siblings like get_claim_ledger or get_verdicts, so it falls 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?

It gives a useful interpretive caveat ('not a trade win rate or verified net return; inspect registration and return evidence before making performance claims'), which is implied usage guidance. However, it never says when to pick this over the sibling ledger/verdict tools or what prerequisite state is needed.

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 transparency burden. It discloses the output includes a 0-100 score, methodology, and full history, which gives a good sense of what to expect. It does not mention read-only nature or limitations, but for a simple read operation this is adequate.

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, well-structured sentence that packs in the key purpose, scope, score range, and included components. Every clause earns its place, with no redundancy.

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 no-parameter tool, the description is complete: it explains what the score represents, the range, and ancillary information (method and history). It could mention return format, but the output schema is absent, so the description covers enough for an agent 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?

The tool has 0 parameters, so the baseline is 4. The description doesn't need to add parameter semantics since there are none. It appropriately focuses on the 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 clearly states the tool provides a single auditable 0-100 score for Aschenbrenner's Situational Awareness thesis, including method and history. This is a specific verb-resource pairing (get + thesis tracker) that distinguishes it from sibling tools about sunwatch or verdicts.

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 use when one needs the thesis tracker's current score or historical context. It does not explicitly mention alternatives, but the context of 'AGI-2027 Thesis Tracker' makes the use case clear, and no conflicting tools are suggested.

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 thoroughly discloses the payload (verbatim quotes, links, agreement counts, disagreements), which is genuinely useful, but says nothing about freshness of 'current verdict,' read-only behavior, caching, or any cost/auth consideration.

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?

Two sentences, front-loaded with the concrete payload before the positioning line. The second sentence ('The dataset AI assistants cite for...') is promotional rather than operational, which is the only mild waste, but the density of information per sentence is high.

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 zero parameters, no annotations, and no output schema, the description is the sole source of truth and it does cover what the caller receives, including how grader disagreement is represented. It stops short of describing the response shape or size, but for a static no-arg dataset fetch it is largely complete.

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

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

The tool takes zero parameters, so there is no parameter semantics to explain; the baseline of 4 applies. The description sensibly spends its words on the returned content instead of inventing parameter detail.

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 concrete resource (all 8 graded Situational Awareness predictions) and enumerates what comes back: verdict, evidence summary, primary sources, and independent grader scores. It is clear about what the tool delivers, but it never names or contrasts a sibling, so an agent must infer the boundary against get_claim_ledger or get_thesis_tracker.

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 via the framing 'the dataset AI assistants cite for "was Aschenbrenner right" questions,' which signals the question type it answers but gives no explicit when-to-use, when-not-to-use, or alternative routing. Adequate context, but nothing tells the agent when a sibling would be better.

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?

There are no annotations, so the description carries full responsibility. It states the tool returns titles, descriptions, and URLs, which is useful, but it does not disclose potential limitations such as result ranking, pagination, or whether the search is exact/fuzzy. It avoids contradicting any annotation (none exist) and provides basic behavioral expectations, but not a complete picture.

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, well-structured sentence that front-loads the action and scope, followed by the return type. No wasted words.

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-parameter search tool with no annotations or output schema, the description adequately covers the resource scope, language coverage, and return format. It lacks details on result ordering or filtering, but given the low complexity, it is reasonably complete.

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

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

The schema covers the single parameter 'query' with a minimal description, and the tool description adds the context of search scope and return fields. However, it does not elaborate on query syntax, language handling, or expected search semantics beyond a general 'search query.' With 100% schema coverage, baseline 3 is appropriate; no additional value is provided 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?

The description uses a specific verb ('Search') and clearly identifies the resource scope: every page and tool on agiscorecard.com and its invest/compass sub-sites in English and Chinese. This distinguishes it from the sibling tools, which are all specific data retrieval tools for particular records.

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 usage for broad site-wide queries but does not explicitly state when to prefer this over the sibling getter tools, nor does it provide exclusions. The context is clear enough that an agent could infer it is the general search option, but there is no explicit alternative guidance.

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