Mode VC: AI Venture Research Charts & Investor Atlas

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 enumerates what is returned (data series, takeaway, notes, primary sources), which partially compensates for the absent output schema, but says nothing about auth requirements, error behavior on an invalid slug, or 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?

A single front-loaded sentence with no filler; the resource and identifier appear early. It is appropriately sized for a one-parameter lookup tool.

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 one required parameter, no output schema, and no annotations, the description covers what the tool returns, which is the main gap an agent would otherwise face. It stops short of covering sibling routing or failure modes.

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 slug parameter, so baseline is 3. The description adds that lookup is possible 'by its slug or title,' but the schema accepts only slug, so this extra claim is at best redundant and at worst mildly misleading.

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 (retrieves) and resource (complete empirical data series, takeaway, notes, primary sources) scoped to a single Mode VC chart. This clearly distinguishes it from search_charts and get_venture_chart_of_the_day, though it never names those siblings.

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

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

The description implies direct lookup by identifier but gives no explicit when-to-use guidance, no exclusions, and no routing to search_charts for discovery vs. this tool for full retrieval. The agent must infer the workflow.

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 does disclose the nature of the output (a 1-5 defensibility score and an AI parity vulnerability assessment). However, it says nothing about determinism, cost, permissions, or the exact return shape, and no output schema fills that 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?

Two tightly written sentences with no filler, front-loading the concrete output (score range and parity vulnerability) before the analytic inputs. Every clause 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?

The description names the two outputs, which partially compensates for the absent output schema, and the schema fully covers inputs. It still omits when-to-use criteria and any behavioral expectations, leaving the definition adequate but with clear gaps for a tool with no annotations.

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, and enum values for business_model are supplied there. The description concept mentions acquisition channels, network effects, and workflow lock-in, but adds no syntax or format detail 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 gives a specific verb (calculates), a concrete resource (defensibility score on a 1-5 scale plus AI parity vulnerability), and names the underlying framework. It is unmistakably distinct from the sibling chart and investor tools without needing to say so.

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, when not to, or which alternative applies. The only contextual cue is the implied use case of evaluating a startup's distribution moat, 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 provided, so the description carries the full burden. It does disclose the response contents (partners, active funds, website, portfolio focus), which is genuinely useful absent an output schema, and 'verified' hints at data trust. But it omits behavior on a missing/ambiguous slug, auth requirements, and 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?

A single well-formed sentence that front-loads the verb and resource before listing the payload. No wasted clauses, though it is a lightly padded enumeration of contents that 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 low-complexity, single-parameter lookup with no output schema, the description covers what the tool does and what it returns, compensating for the missing return schema. The main gap is routing guidance relative to search_investors and the thesis sibling, which keeps it short of 5.

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

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

Schema description coverage is 100% and the single parameter already documents accepted forms with concrete slug examples. The description only restates 'a specific venture capital firm' and adds no syntax or format meaning beyond the schema, 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 ('Retrieves') and resource ('complete verified profile for a specific venture capital firm') and enumerates what the profile contains. An agent can tell it returns a single firm's record rather than a list. However, it never names or contrasts with siblings like search_investors or get_mode_vc_thesis_and_criteria, so the boundary is left implicit.

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 a specific venture capital firm' implies the caller must already know which firm, contrasting implicitly with search_investors, but this is never made explicit. There is no statement of when to use this versus the thesis or search tools, nor any prerequisite guidance beyond the required identifier.

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 and no output schema, the description carries the full burden, and it does disclose the substantive content of the return (ticket range, mandate, framework, routing). It is silent on format, freshness, and whether the data is static, but for a zero-parameter read tool the key behavioral trait — what comes back — is covered.

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 resource, with no filler or repetition. Every clause adds a distinct content category.

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 parameterless lookup with no annotations or output schema, the description gives enough for an agent to know what it will receive. It falls slightly short on vague terms like 'evaluation framework' and 'direct pitch routing', which are not unpacked, and on return format.

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 no parameters at all (0 params, empty object schema), 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?

States a specific verb ('Returns') and a specific resource ('Mode VC's investment parameters'), then enumerates the payload: ticket sizes, geographic mandate, evaluation framework, pitch routing. It does not differentiate itself from siblings like get_investor_details, which is the only thing keeping it out of 5 territory.

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 statement and no named alternative. Usage is only implied by the tool name and the enumerated fund-specific content, which an agent can infer but must do so on its own.

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. 'Returns' implies a read operation and 'latest daily' hints at refresh cadence, but there is no disclosure of safety profile, whether results are a fixed curated set or paginated, or how the source lab constrains the data.

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-loading the verb and the content domains. It is appropriately sized for a simple retrieval tool.

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, zero-required-param tool with full schema coverage, the description is minimally adequate. However, with no annotations and no output schema, it does not describe the shape of a returned 'chart' or whether results are curated vs. filtered, leaving an agent to guess at the return contract.

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 'limit' parameter is fully documented in the schema, including default and min/max bounds. The description adds no additional meaning about the parameter, 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 ('Returns') and a specific resource (daily AI adoption, developer trends, benchmark, pricing charts) from a named source, so the agent knows exactly what it gets. It does not explicitly distinguish itself from 'Chart of the Day' vs. the sibling get_chart_data or search_charts, so sibling differentiation is left to the name/title.

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 at all — no statement of when to pick this over get_chart_data or search_charts, and no prerequisites. Usage is only implied by the word 'daily'.

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 add real value by enumerating what comes back (titles, empirical claims, takeaways, canonical URLs, markdown endpoints), which is important since there is no output schema. It still omits the safety profile (read-only nature), result ordering, rate limits, and pagination behavior, and it advertises date filtering that the schema does not 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?

Two tight sentences, front-loaded with what is searched, then what is returned. Every clause is informational, and the return-field sentence is justified by the absence of an output schema; only minor redundancy with the title keeps it from a 5.

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-parameter, no-output-schema search tool this covers purpose, filter dimensions, and return contents, which is most of what an agent needs. The unsupported "date" filter claim and the absence of any pagination or ordering note are the only 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 limit and query are fully documented in the schema and the baseline is 3. The description adds little beyond that and actually introduces a mismatch: it promises filtering by "date" when no date parameter exists, which could mislead an agent about what the tool accepts.

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 ("Searches") plus resource and scope ("all 38 empirical AI & venture research charts published by Mode VC"), which the title reinforces. The verb "searches across all" implicitly contrasts with the get_* siblings that retrieve a single item, but no sibling is named explicitly, so it stops short of full differentiation.

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?

"Filter by keyword, topic, or date" implies how to narrow results, which is adequate for a search tool. However, it never says when to use this versus get_chart_data or get_venture_chart_of_the_day, and gives no exclusions or preconditions, so usage is only implied.

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 says nothing about result ordering, match behavior (fuzzy vs exact), pagination beyond the schema's limit, or that this is a read-only lookup. It discloses corpus size and provenance, which is useful context but not runtime 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?

Two tightly written sentences with the coverage scope front-loaded and the filterable dimensions second. No filler, no repetition of the title.

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 five-parameter search tool with no annotations and no output schema, the description conveys the corpus and filter axes but omits what a result actually contains and how results are ordered or truncated. Adequate to call the tool, but not 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 every parameter (limit, query, stage, sector, location) is already documented with examples and an enum, setting the baseline at 3. The description only restates the filterable dimensions and does not clarify, for example, that query matches firm OR partner name simultaneously, nor anything about limit semantics.

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

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

States a specific verb (search) and a precise resource (institutional VC firms and partners) and quantifies coverage (3,650+ firms, 13,000+ partners from the Mode VC Investor Atlas). It never names the sibling get_investor_details, so an agent must infer that this tool searches while that one retrieves details.

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 'Filter by firm name, partner name, sector, stage, or geography' implies when the tool is useful, but there is no explicit when-to-use/when-not guidance and no routing to alternatives such as get_investor_details for a known firm or get_mode_vc_thesis_and_criteria for thesis data.

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