PV Solaire Énergie — Estimation solaire et devis (France)

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

Beyond the annotations, the description discloses the 30-minute code validity, the five-attempt limit, and the fact that this validation is the sole authorization for forwarding the request to installers. It also explains the no-duplicate consequence of retrying demander_devis, adding valuable behavioral context.

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?

Three dense sentences cover purpose, workflow, user interaction, and edge-case handling without redundancy. The most important information is front-loaded.

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 tool with no output schema, the description supplies all necessary workflow context: input provenance, retry conditions, expiration, and downstream side effect. Nothing needed to invoke 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?

The schema already covers both parameters at 100%, so the baseline is 3; the description adds meaningful provenance semantics by stating demande_id comes from demander_devis, that the code must be requested from the user rather than guessed, and that retries do not create duplicates.

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 action ('Valide une demande de devis') with a precise mechanism (4-digit SMS code) and names the related tool that creates the request. It clearly distinguishes itself from demander_devis, and the title reinforces the same purpose.

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 explicitly instructs the agent to ask the user for the code, call the tool with the demande_id from demander_devis, and provides the exact fallback flow if the code expires or after five failed attempts. This is model guidance on when and how to use the tool.

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?

The description clearly discloses side effects beyond annotations: it sends a 4-digit SMS code, returns a demande_id, and does not transmit the request to any installer until confirmer_demande validates it. It also warns the agent not to tell the user the request was sent prematurely, which is critical behavioral context not present in the annotations.

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 dense but every sentence earns its place: purpose, two-step workflow, user-facing constraint, data integrity rule, and prerequisite call. The critical warnings are front-loaded after the purpose statement, making the essential behavior immediately visible.

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?

Despite having 17 parameters and no output schema, the description provides enough operational context: the return value is named (demande_id), the next step is named (confirmer_demande), the prerequisite is named (estimer_production_solaire), and the no-test-data rule is stated. The schema covers parameter details, so nothing needed for correct invocation 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?

Schema description coverage is 100%, so the schema already documents all 17 parameters. The description adds value by explaining that estimation_id should be obtained first and makes quotes more precise, but it does not substantially expand per-parameter semantics 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 first sentence states a specific action, resource, and scope: depositing a free quote request for solar panel installation in France. It further distinguishes itself from its sibling confirmer_demande by explicitly labeling itself as 'PREMIÈRE ÉTAPE SUR DEUX' and explaining it only returns a demande_id.

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 workflow guidance: call estimer_production_solaire first and pass estimation_id, request the SMS code from the user, then call confirmer_demande. It also states clear exclusions and guardrails: never call without explicit consent, never use invented or test data, and never announce the request as sent before confirmation.

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 and openWorldHint. The description adds meaningful behavioral context: the analysis is based on real 3D geometry (Google Solar), computed via PVGIS, and explicitly states that results are not a regional average or a rule-of-three. It also discloses that the heat map is returned only 'quand elle est disponible', consistent with openWorldHint.

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 focused paragraph with a strong front-loaded action and output list. The clarification 'ce n'est ni une moyenne régionale ni une règle de trois au m²' earns its place by setting expectations. It is slightly long but every sentence adds value; a 4 reflects good structure with minor room for tighter phrasing.

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 covers the input condition (French postal address), the computation method, the full set of returned fields, the optional heat map, the estimation_id link to demander_devis, and explicit usage triggers. Since there is no output schema and no other structured description of results, this text carries that burden and does so thoroughly for a 2-parameter read-only tool.

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

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

Schema coverage is 100% and the schema descriptions are already rich, including an example for adresse and a note that type_batiment changes results. The main description emphasizes 'à partir de sa seule adresse postale' but adds no new meaning about either parameter 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 opens with a specific verb ('Calcule') and a precise resource ('le potentiel photovoltaïque réel d'une toiture'), then lists concrete output fields (kWc, kWh, panneaux, orientation, etc.). It clearly distinguishes itself from siblings by mentioning the estimation_id passed to demander_devis, leaving little ambiguity about its role.

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 explicitly states when to use the tool: 'Utilise cet outil dès qu'un utilisateur demande combien produirait une installation solaire chez lui...' It also gives contextual guidance in the schema about requesting a precise address before calling. However, it does not explicitly say when not to use it or name alternatives, so it stops short of a 5.

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

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

The readOnlyHint and openWorldHint annotations already signal a safe, open-world read; the description adds substantial behavior beyond that: it uses a live external directory, filters to professionals with currently valid RGE qualification, returns results ordered by distance, and lists the exact output fields. No contradiction with annotations.

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 front-loaded with the core search action, followed by the crucial RGE filter, output contents, and usage guidance. Every sentence earns its place, and it remains compact despite covering multiple important facets.

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?

Since there is no output schema, the description usefully explains what each result contains: name, city, distance in km, qualification end date, and profile URL. With the schema covering all parameters and annotations covering the read-only/open-world nature, nothing essential is missing for an agent to select and invoke this tool correctly.

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

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

Schema description coverage is 100%, so the baseline is 3. The description reinforces the idea of proximity and the RGE filter, but it does not add parameter-level details beyond what the schema already provides clearly for lieu, type, limite, and rayon_km.

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: it searches the pv-solaire-energie.com directory for French solar installers and engineering firms around a commune or postal code, sorted by distance. It clearly distinguishes this search tool from the sibling tools, which handle confirmations, quote requests, and production estimation.

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 explicitly states when to use the tool: when a user looks for a solar professional in their region, wants to check for certified installers nearby, or compares companies before requesting a quote. It gives clear context but does not explicitly mention when not to use it or name alternatives.

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