GPU and LLM inference benchmarks, hardware evidence, deployment recommendations, and launch configs.
Average 3.3/5 across 5 of 5 tools scored. Lowest: 2.4/5.
Each tool has a clear, distinct stage in the deployment advisory workflow: searching evidence, checking fit, predicting performance, recommending purchases, and generating launch configs. There is no meaningful overlap between tool purposes.
All tool names follow a consistent verb_noun pattern: check, generate, predict, recommend, and search paired with clear objects. Naming style is uniform and predictable.
Five tools is well-scoped for the advisory domain, covering the full pipeline from evidence search through config generation. Each tool has a distinct role and none feels redundant or missing.
The tool surface covers the complete advisory lifecycle: finding evidence, assessing fit, predicting performance, forming recommendations, and producing launch templates. There are no obvious dead ends or missing operations for the stated purpose.
check_deployment_fitB$0.01 USDC on Base mainnet: check model/configuration fit against bounded StackBench capacity evidence.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | ||
| engine | No | ||
| concurrency | No | ||
| max_evidence | No | ||
| quantization | No | ||
| context_length | Yes | ||
| hardware_system | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safe read-only, idempotent, non-destructive profile. The description adds meaningful operational context by disclosing a $0.01 USDC cost on Base mainnet and noting that the evidence base is 'bounded,' which are traits not captured in the structured 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 a single compact sentence with the core operation stated after the cost prefix. It is appropriately sized, though the '$0.01 USDC on Base mainnet' opener slightly delays the actual behavior.
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 an output schema and safety annotations, the tool has seven parameters, zero schema descriptions, and no guidance on when to use it relative to its siblings. The description leaves the meaning of 'fit,' the role of max_evidence, and the relationship to concurrent/quantization settings unexplained.
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%, so the description must compensate for seven undocumented parameters, but it only refers generically to 'model/configuration fit.' Parameters such as hardware_system, context_length, concurrency, quantization, and max_evidence are not semantically expanded beyond their names and JSON types.
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 operation—'check model/configuration fit against bounded StackBench capacity evidence'—making the core function clear. It doesn't explicitly name a sibling, but the verb 'check' and the focus on 'fit against evidence' distinguish it from generate, predict, recommend, and search.
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 is provided, and no sibling tools are referenced. An agent cannot tell from the description whether to choose this over predict_performance or recommend_deployment.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_launch_configA$0.50 USDC: generate deterministic vLLM or llama.cpp launch templates from an exact StackBench recommendation without claiming a launch occurred.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | ||
| runtime | Yes | ||
| objective | No | balanced | |
| batch_size | No | ||
| concurrency | No | ||
| ubatch_size | No | ||
| quantization | No | ||
| prompt_tokens | Yes | ||
| engine_version | No | ||
| evidence_policy | No | include_public | |
| flash_attention | No | ||
| hardware_system | Yes | ||
| constraint_policy | No | point_estimate | |
| maximum_vram_bytes | No | ||
| maximum_power_watts | No | ||
| candidate_kv_formats | No | ||
| decode_context_depth | No | ||
| minimum_decode_tok_s | No | ||
| minimum_prefill_tok_s | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful behavior beyond those: a $0.50 USDC cost, determinism, and an explicit statement that no launch is claimed to have occurred. These are useful operational details 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 a single sentence with no filler. It front-loads the cost and core purpose and adds a meaningful behavioral qualifier about not claiming a launch. 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?
Despite having an output schema and strong annotations, this is a complex tool with 19 parameters and 4 required ones. The description does not explain how to supply or interpret the 'exact StackBench recommendation,' nor does it clarify how the deterministic templates are derived from required inputs. It is adequate for basic gisting but not fully complete for correct invocation.
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%, so the description needed to compensate, but it does not. It names vLLM and llama.cpp, which hints at the 'runtime' parameter, but the other required parameters — model, hardware_system, and prompt_tokens — are not explained. 'Exact StackBench recommendation' is too vague to convey how the 19 parameters map to the call.
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 ('generate'), names the resource ('deterministic vLLM or llama.cpp launch templates'), and identifies the input source ('exact StackBench recommendation'). It also explicitly distinguishes itself from tools that would claim a launch occurred, which separates it from siblings like recommend_deployment and check_deployment_fit.
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 'from an exact StackBench recommendation' implies this tool should be used after a recommendation already exists, which gives some usage context. However, it never explicitly names alternatives or states when not to use this tool versus siblings like recommend_deployment or predict_performance. The guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
predict_performanceB$0.05 USDC: predict configuration throughput, memory, and power from bounded StackBench evidence with confidence, missing metrics, and caveats.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | ||
| engine | No | ||
| batch_size | No | ||
| concurrency | No | ||
| ubatch_size | No | ||
| max_evidence | No | ||
| quantization | No | ||
| prompt_tokens | Yes | ||
| engine_version | No | ||
| flash_attention | No | ||
| hardware_system | Yes | ||
| kv_cache_type_k | No | ||
| kv_cache_type_v | No | ||
| requested_metrics | No | ||
| decode_context_depth | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already flag read-only, idempotent, non-destructive behavior. The description adds valuable context beyond those: it is priced, limited to 'bounded StackBench evidence,' and returns confidence scores, missing metrics, and caveats. 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?
A single dense sentence packs cost, scope, evidence source, output characteristics, and caveats with no filler. It is front-loaded and every part 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 tool with 15 parameters, 3 required inputs, and zero schema descriptions, the description is not enough to invoke correctly. It does not mention required inputs, how parameters map to the prediction, or how the evidence bound (max_evidence) affects results, though output schema covers return shape.
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%, so the description carries the full burden for explaining 15 parameters. It broadly suggests that inputs form a 'configuration' and mentions throughput/memory/power outputs, but it does not clarify the meaning or usage of model, hardware_system, prompt_tokens, engine, quantization, or the other fields.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies a clear action (predict) and resource (configuration throughput, memory, and power) plus the evidence source (bounded StackBench evidence). It does not explicitly contrast with sibling tools, so it misses the top bar for 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?
There is no guidance on when to use this tool instead of check_deployment_fit, generate_launch_config, recommend_deployment, or search_evidence. The only contextual clue is the $0.05 cost, which does not help an agent choose among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recommend_deploymentC$0.25 USDC: recommend what to buy or run from bounded StackBench fit, performance, confidence, evidence, caveats, and economics only when supported.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | configuration | |
| model | Yes | ||
| engine | No | ||
| objective | No | balanced | |
| tco_years | No | ||
| batch_size | No | ||
| concurrency | No | ||
| cost_policy | No | latest_official | |
| max_results | No | ||
| ubatch_size | No | ||
| quantization | No | ||
| budget_amount | No | ||
| prompt_tokens | Yes | ||
| engine_version | No | ||
| ownership_mode | No | purchase | |
| quality_policy | No | prefer | |
| budget_currency | No | ||
| evidence_policy | No | include_public | |
| flash_attention | No | ||
| hardware_system | No | ||
| workload_profile | No | ||
| constraint_policy | No | point_estimate | |
| maximum_tco_amount | No | ||
| maximum_vram_bytes | No | ||
| quality_thresholds | No | ||
| maximum_power_watts | No | ||
| candidate_kv_formats | No | ||
| decode_context_depth | No | ||
| minimum_decode_tok_s | No | ||
| recommendation_scope | No | full | |
| manual_hardware_costs | No | ||
| minimum_prefill_tok_s | No | ||
| materiality_tiebreaker | No | minimize_power | |
| quality_policy_version | No | planner-quality-policy-v2 | |
| quality_evidence_policy | No | allow_calibrated_estimates | |
| economic_materiality_pct | No | ||
| electricity_rate_per_kwh | No | ||
| workload_profile_version | No | ||
| utilization_days_per_year | No | ||
| utilization_hours_per_day | No | ||
| economic_materiality_amount | No | ||
| allow_manual_market_override | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds that recommendations are drawn from 'bounded StackBench fit... only when supported,' which is a useful boundary, but it does not explain output behavior, limitations, or the meaning of the '$0.25 USDC' prefix.
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 short, but it is not well-structured for an agent. The unexplained '$0.25 USDC:' prefix consumes space without adding clarity, and 'only when supported' is ambiguous. The core verb is front-loaded, but the remaining content is vague and under-specified.
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 42 parameters, 10 enums, and four siblings, this single-line description is severely incomplete. It omits what 'StackBench' and 'bounded' mean, how recommendations are produced, what inputs are expected, and what constraints apply. The output schema helps, but it cannot compensate for the lack of domain and workflow context.
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?
With 42 parameters and 0% schema description coverage, the description needed to explain how parameters relate to the recommendation process, but it mentions none of them. The phrase 'fit, performance, confidence, evidence, caveats, and economics' hints at concepts, but provides no concrete parameter-level meaning or guidance.
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 core action: 'recommend what to buy or run', and it names the input dimensions considered (fit, performance, confidence, evidence, caveats, economics). This distinguishes it from sibling tools like search_evidence or predict_performance, though it does not explicitly call out the differences.
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 about when to use this tool versus alternatives such as check_deployment_fit, generate_launch_config, or predict_performance. The phrase 'only when supported' hints at a condition, but it is vague and does not say when the recommendation should be sought or when a sibling is more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_evidenceA$0.00 free search of bounded public StackBench benchmark and failure evidence; use this for evidence-only requests.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and non-destructive behavior, so the description only needs to add context beyond those flags. It adds useful scope and cost details ('free', 'bounded public StackBench benchmark'), but it does not discuss pagination, result limits, accessibility, or any operational behaviors beyond the simple search.
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 entire description is a single front-loaded sentence containing the key facts: cost, scope, bounded nature, and intended usage. There is no filler, repetition, or irrelevant detail.
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 search tool with an existing output schema, the description covers all necessary calling context: what data is searched, that it is free and bounded, and the kind of requests it supports. Nothing critical is missing for an agent to decide whether to use this 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 0%, so the description carries significant responsibility for explaining parameters. It does not explicitly describe 'query' or 'limit' semantics. The search context makes 'query' reasonably inferable, and JSON schema constraints document 'limit', but the description itself adds little direct meaning to the parameters.
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 specific action ('search'), the resource ('bounded public StackBench benchmark and failure evidence'), and distinguishes its purpose from the sibling tools by framing it for 'evidence-only requests.' This prevents confusion with decision-oriented siblings like recommend_deployment or predict_performance.
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 offers a clear positive instruction: 'use this for evidence-only requests.' It does not explicitly list exclusions or name alternative tools, but the use case is clear enough given the sibling tool names and the focused scope described.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
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The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
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Verified hardware specifications (GPU, CPU, PSU) for autonomous agents. No scraping needed.
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